Report on new Pléiades Neo sensors benchmark : geometric benchmarking over Maussane test site for CAP 2014+ purposes.
Date: 2023-10-19
Institutions: Joint Research Centre
Subjects: common agricultural policy, data collection, data processing, geographical information system, remote sensing, research report, satellite

ISSN 1831-9424 

JRC TECHNICAL REPORT 

Report on new Pléiades Neo sensors 
benchmark 

Geometric benchmarking over Maussane test site for CAP 2014+ 
purposes 

Loudjani, P., Lemajic, S., .Parage, V., Rodrigues, C. 

2023 

 EUR 31687 EN

 
 
 
 
 
 
 
 
 
 
 
  
 
This publication is a Technical report by the Joint Research Centre (JRC), the European Commission’s science and knowledge service. It aims 
to  provide evidence-based scientific support to the European policymaking  process.  The contents of this publication do not necessarily 
reflect the  position or  opinion of the European Commission. Neither the European Commission nor  any  person acting on behalf of the 
Commission is responsible for the use that might be made of this publication. For information on the methodology and quality underlying 
the data used in this publication for which the source is neither Eurostat nor other Commission services, users should contact the referenced 
source. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever 
on the part of the European Union concerning the legal status of any country, territory, city or area or of its authorities, or concerning the 
delimitation of its frontiers or boundaries. 

Contact information 
Name: Philippe Loudjani 
Address: Joint Research Centre, Via Enrico Fermi 2749, TP 272, 21027 Ispra (VA), Italy 
Email: philippe.loudjani@ec.europa.eu 
Tel.: 39 0332 78 6160 

EU Science Hub 
https://joint-research-centre.ec.europa.eu 

JRC134704 

EUR 31687 EN 

PDF ISBN 978-92-68-08187-7     ISSN 1831-9424     doi:10.2760/38474      KJ-NA-31-687-EN-N 

Ispra: European Commission, 2023 

© European Union, 2023  

The reuse policy of the European Commission documents is implemented by the Commission Decision 2011/833/EU of 12 December 2011 
on the reuse of Commission documents (OJ L 330, 14.12.2011, p. 39). Unless otherwise noted, the reuse of this document is authorised 
under the Creative Commons Attribution 4.0 International (CC BY 4.0) licence (https://creativecommons.org/licenses/by/4.0/). This means 
that reuse is allowed provided appropriate credit is given and any changes are indicated.  

For any use or reproduction of photos or other material that is not owned by the European Union permission must be sought directly from 
the copyright holders. The European Union does not own the copyright in relation to the following elements: 
All illustrations in this report were produced by the authors of the report. 

How to cite this report: Loudjani P., .Lemajic S., Parage V., and Rodrigues C., Report on new Pléiades Neo sensors benchmark, European 
Commission, Ispra, 2023, doi:10.2760/38474, JRC134704. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Contents 

Abstract ....................................................................................................................................................................................................................................................................... 1 

Executive summary .......................................................................................................................................................................................................................................... 2 

1 

Introduction..................................................................................................................................................................................................................................................... 3 

2  Pléiades Neo satellite ........................................................................................................................................................................................................................... 4 

3  Pléiades Neo image products (information provided by Airbus) ................................................................................................................. 6 

3.1  Spectral Band Combinations ............................................................................................................................................................................................ 6 

3.2  Geometric Processing Levels ........................................................................................................................................................................................... 7 

4  Study Area ....................................................................................................................................................................................................................................................... 9 

5  Auxiliary data and imagery used ............................................................................................................................................................................................ 10 

5.1  Ground Control Points .......................................................................................................................................................................................................... 10 

5.2  Digital Elevation Model (DEM) ...................................................................................................................................................................................... 14 

5.3  Aerial Orthomosaics used ................................................................................................................................................................................................ 15 

5.4  Pléiades Neo satellite imagery used ..................................................................................................................................................................... 15 

5.5  Software............................................................................................................................................................................................................................................ 17 

6  Pléiades Neo Benchmarking Tests ........................................................................................................................................................................................ 19 

6.1  Benchmarking Methodology .......................................................................................................................................................................................... 19 

6.2  Test Scenarios ............................................................................................................................................................................................................................. 19 

6.3 

Internal Quality Control (Airbus) ................................................................................................................................................................................ 21 

7  External Quality Control ................................................................................................................................................................................................................... 26 

7.1  Method for external quality check (EQC) of ortho imagery ............................................................................................................. 26 

7.1.1 

Independent check points (ICPs) selection and distribution .......................................................................................... 26 

7.1.2 

Geometric quality assessment-measurements and calculations ............................................................................ 28 

8  Results and discussions ................................................................................................................................................................................................................... 30 

8.1 

Impact of the orthorectification model ............................................................................................................................................................... 30 

8.2 

Impact on the number of GCPs used for the image processing ................................................................................................. 33 

8.3 

Impact of software used ................................................................................................................................................................................................... 36 

8.4 

Impact of image acquisition angles ....................................................................................................................................................................... 37 

8.5 

Impact of Pleaides sensors ............................................................................................................................................................................................. 39 

8.6  Summarising values .............................................................................................................................................................................................................. 39 

9  Conclusions .................................................................................................................................................................................................................................................. 41 

References ............................................................................................................................................................................................................................................................. 42 

List of abbreviations and definitions ........................................................................................................................................................................................... 43 

List of figures ..................................................................................................................................................................................................................................................... 44 

List of tables ........................................................................................................................................................................................................................................................ 46 

i 

 
 
 
 
 
 
 
Abstract 

In order to be used in the frame of the Common Agricultural Policy (CAP) 2014+ direct payments’ management, 
all Very High Resolution (VHR) imagery should comply with spatial accuracy compatible with a scale of 1:5000 
or larger. To check  this, the JRC has set a benchmarking  test to verify whether  the  planimetric accuracy  of 
produced ortho imagery of any new VHR sensor does not exceed certain thresholds. 

The JRC has been contacted by Airbus Defence and Space Intelligence Company to test their new Pléiades Neo 
VHR sensors. The benchmarking tests were carried out on six Pléiades Neo 3 and 4 imagery collected between 
February and May 2022. This report describes in detail how the tests were performed i.e. auxiliary data used, 
methodology and workflow as well as outcome from the Internal Quality Control. Tests are performed by the 
sensor Company itself but, to make the tests objective, the JRC also performs an external Quality Control which 
is a base for certification of the sensor.  

The results of these tests allowed to conclude that the imagery collected by recently launched Pléiades NEO 
satellites can be potentially used in the frame of The CAP 2014+ image acquisition campaign. This conclusion 
remains valid under certain conditions of image acquisition angles and ortho rectification process. 

(To note that the benchmarking tests have been set for the CAP 2014+ technical specifications and were valid 
until  31  December  2022.  For  the  CAP  2023+,  entering  into  force  on  1st  January  2023,  new  technical 
specifications will be set to comply with the new VHR imagery use requirements i.e. in the frame of Quality 
Assessment processes). 

Authors 

Loudjani  P. (European Commission, Joint Research Centre  (JRC), Ispra, Italy),  Lemajic  S.  (Fincons  SpA,  Milan, 
Italy), Parage V., and Rodrigues C. (Airbus Defence and Space Intelligence, Toulouse, France) 

1 

 
Executive summary 

The main objective of the present report is to assess whether the new Very High Resolution (VHR) Pléiades Neo 
sensors can be qualified for Control with Remote Sensing (CwRS) programme, in Common Agriculture Policy 
(CAP). The benchmarking presented herein aims at evaluating the usability of Pléiades Neo for the CAP checks 
through an estimation of its geometric (positional) accuracy, as well as measuring the influence of different 
factors (viewing angle, number of GCPs, software implementation) on this accuracy. For that purpose, apart 
from checking the results of the Internal Quality Control of Airbus Company, an independent External Quality 
Control is done to verify if the orthoimagery conforms to the standard method developed by JRC. 

Benchmarking tests were carried out on 6 images acquired over an Area of Interest (AOI) close to Maussane 
(France). For the two sensors (Pléiades Neo 3 and 4) 3 images have been acquired, between February and May 
2022, with three configurations of acquisition to test the orthorectification process. 

This report describes in detail how the tests were performed i.e. auxiliary data used (chapter 5), methodology 
and workflow (chapter 6). 

The outcome from the Internal Quality Control performed by Airbus are given in chapter 6.3. The details of the 
independent External Quality Control handled by JRC are provided in Chapter 7 while the results are detailed in 
Chapter 8.  

In the light of the benchmark tests performed on Pléiades NEO ortho products, one can draw the 
conclusion that the Neo ortho imagery geometric accuracy potentially meets the requirements of 
RMSEx  ≤1.25m  and  RMSEy  ≤1.25  for  the  VHR  prime  profiles  defined  in  the  VHR  profile  based 
technical specifications. 

These results were obtained for tests with imagery acquired with 5˚, 20˚ and 30˚ Off Nadir Angles (ONA), with 
at least 3 GCPs use for orthorectification and with RPC or rigorous models. 

It must be noted that when rectification were made without GCPs, results obtained become out of specifications 
for acquisitions 30˚ ONA and even 20˚ in some case for Pléiades Neo4 sensor. 

The reservation made on fully meeting the requirements lies on the fact that, sometimes, atypical results were 
obtained such as higher RMSE observed for near nadir acquisition compared to 20˚ or 30˚ ONA. However, the 
number of images made available was insufficient to further investigate this phenomenon. 

To note also, that at the time of the benchmark analysis, Airbus production chain was not yet fully finalised and 
internal references were not yet implemented.  Airbus recently informed us that, according to the tests they 
performed using internal references that will be implemented soon in its production chain, the results are within 
the specifications whatever the PNEO sensor for angles up to 30° ONA. This should be further confirmed but 
the JRC benchmarking work was already concluded at the time of this notification. 

Finally, one should remind that the available JRC’s GCPs dataset is now dating. Several points became unreliable 
since changes occurred on the ground. Even if these points were generally discarded, one can argue that RMSEs 
estimated may hold some uncertainty. 

Policy context 

The benchmarking tests were set to be used in the frame of the Common Agricultural Policy (CAP) 2014+ direct 
payments’ management. The aim was to verify that all used Very High Resolution (VHR) imagery would comply 
with spatial accuracy compatible with a scale of 1:5000 or larger. 

The benchmarking tests were performed by JRC, under an Administrative Arrangement with DG Agriculture, and 
upon request of image providers proposing new VHR sensors. This Benchmarking activity has thus ceased on 
the 31st December 2022 with the ending of the CAP2014+. 

For  the  CAP  2023+,  new  technical  specifications  will  be  set  to  comply  with  the  new  VHR  imagery  use 
requirements i.e. in the frame of Quality Assessment processes. Should sensor benchmarking tests be put in 
place, they would need to be done according to these up-dated technical specifications. 

2 

 
 
1 

Introduction 

This  report describes in details steps that have been taken to check if Pléiades Neo sensors  qualify for the 
Common Agricultural Policy (CAP) image acquisition Campaign. The main requirement according to VHR image 
acquisition specifications for the CAP 2014+ checks [ii] is to have a spatial resolution compliant with at least a 
scale of 1:5000 or larger. This translates into a required the planimetric accuracy of: 

 

 

RMSEx ≤1.25m and RMSEy ≤1.25 for VHR Prime 

RMSEx ≤5m  and RMSEy ≤5m  for VHR Backup 

Several scenarios are built to test the possible influence of different factors on the ortho imagery accuracy, i.e.: 

 

 

 

number and distribution of Ground Control Points (GCPs); 

incidence angle; 

and the sensor model implemented in the software (ENVI and ERDAS). 

3 

 
 
2  Pléiades Neo satellite 

From the information provided by Airbus Defence and Space Intelligence Company (later on in the text called 
Airbus), Pléiades  Neo  is a  constellation of potentially  four identical, very  high-resolution optical satellites. 2 
were launched in 2021 and 2 others to be launched in late 2022 on board Arianespace’s Vega rocket but the 
launch failed. With a 10-year expected lifetime, they will ensure the provision of 30cm resolution imagery until 
at least 2032, and consequently the continuity of Airbus Intelligence’s high standard geo-information services. 
Table 1. General characteristics of the Pléiades Neo constellation 

Number of satellites 

Four identical satellites in constellation, named: 

 
 
 
 

Pléiades Neo 3 
Pléiades Neo 4 
Pléiades Neo 5 (not available) 
Pléiades Neo 6 (Not available) 

Launch mass 

Body: 1 ton for each satellite 

Launch 

Mission lifetime 

2021 for Pléiades Neo 3 & 4 
2022 for Pléiades Neo 5 & 6 (launch failed) 
10 years for each satellite 
Source: Airbus 

Pléiades Neo operates as a genuine constellation on the same orbit and phased 90° from each other. Added to 
their oblique incidence capability (up to 52° angle) and exceptional agility, this orbit phasing allows the satellites 
to visit any point on the globe twice daily – ideal for monitoring sensitive sites, anticipating risks, effectively 
managing crises and for coverage of large areas. 

Table 2. Orbital characteristics and viewing capability 

Sun-synchronous, 10:30 a.m. descending node 

Orbit 

Altitude 

Cycle 
Period/Inclination 
Frequency of visit  
(with four satellites) 
Acquisition capability 
Nominal imaging mode 
Stereo capability 

620km 

26 days 
97.2°/97.9° 
Twice a day, anywhere 

Up to 2 million km² per day (with four satellites) 
14km-swath strips oriented along North-South axis 
Single-pass stereo and tri-stereo (fore., nadir and aft. mode) 

Source: Airbus 

The twice-daily visit capacity is backed by a very reactive operational loop. Work plans are updated every 25 
minutes and pooled: when an image is to be collected by one satellite, the related acquisition request is removed 
from the tasking plans of the other satellites. These multiple and synchronised daily work plans enable easy 
handling  of  last-minute  tasking  requests,  as  well  as  integration  of  the  latest  weather  information  for  an 
improved data collection success rate. 
In addition, Airbus’s network of ground receiving stations enable an all-orbit contact and ensure near real-time 
performances worldwide and rapid data-access, to deliver the highest standards in our service response times. 
Images are downlinked at each orbit, automatically processed and quickly delivered to the customer, allowing 
faster results in emergency situations. 
For the user, this results in: 

  More image collection opportunities. 
 
 
  Unprecedented capacity for disaster response, regular or intensive monitoring, or change detection. 

Improved map update capacity (coverage). 
Rapid access to data after acquisition. 

4 

 
 
 
 
 
 
 
 
 
 
 
 
 
Table 3. Key attributes of the Pléiades Neo Constellation 

Main X-band receiving stations 

Toulouse (France) 
Kiruna (Sweden) 
Inuvik (Canada) 

S-band uplink stations 

Programming centre 

Production centre 
Tasking 
plans 
frequency 
Update of weather forecast 

refresh 

Kiruna (Sweden) 
Inuvik (Canada) 
Airbus/Geo-Intelligence – Toulouse (France) 
Airbus/Geo-Intelligence – Chantilly VA (USA) 
Airbus DS Intelligence – Toulouse (France) 
Every orbit: 15 times/day/satellite  
(60 times a day in total) 
4 times a day for global weather forecast  
+ at each orbit: weather update for the next 2 hours  

Satellite Control Centre 

Toulouse (France) 

Source: Airbus 

As advertised by Airbus, the Pléiades Neo satellites benefit from exceptional performance in terms of agility 
(roll/pitch: 10° in 7 seconds, 30° in 12 seconds, 60° in 20 seconds). The time required to slew 200 kilometres 
(17°)  is  reduced  to  10  seconds,  including  stabilisation  time.  This  kind  of  performance  results  in  a  reduced 
average acquisition window for the users, allowing more images to be collected during the same pass. Collection 
opportunities are more numerous, conflicts between contiguous requests are minimised, and the acquisition on 
the same pass of either big Area of Interests (AOIs) or several targets at the same latitude is improved. 
The Pléiades Neo constellation offers a wide range of products and services featuring different options to match 
as closely as possible to any customer requirements. Combining the Panchromatic and  multispectral bands, 
images can be visualised as either black and white (30 cm product resolution), natural colours, false colours 
(1.2m  product  resolution)  or  as  merged  product  (Pan-sharpened  colour  image)  with  the  resolution  of  a 
Panchromatic image. 

5 

 
 
 
 
 
 
 
 
 
3  Pléiades Neo image products (information provided by Airbus) 

3.1  Spectral Band Combinations 

Pléiades  Neo  delivers  ready-to-use  products,  which  can  be  easily integrated in GIS  and/or  transformed into 
thematic  information  when  combined  with  other  satellite,  airborne  or  ground  information.  Pléiades  Neo 
satellites always acquire images simultaneously in two modes: 

  Panchromatic: one band (black and white with 30 cm product resolution) 
  Multispectral: six bands (colour with 1.2m product resolution) 

Compared to Pléiades satellites generation, Pléiades Neo has two additional multispectral bands: 

  Deep-blue: allows deeper penetration into water bodies, and is very useful in bathymetric studies, 

and has better characterisation of atmospheric conditions and discrimination in shadows. 

  Red-edge: is particularly useful for agricultural and vegetation-related applications as it provides 

crop and vegetation status through photosynthesis characterisation. Once canopy closure is reached 
(NDVI is saturating), Red-edge can differentiate between the various densities of canopy. 

The Multispectral products are offered as four or six-band products: 

  MS: Multispectral (4 bands RGB+NIR) 
 

Full MS: Multispectral (6 bands RGB+NIR+ Deep-blue + Red-edge) 

Panchromatic and Multispectral image products are co-registered (completely superimposable). 

Pan-sharpened Products: 
Pan-sharpened Products are the products provided for the different test of this benchmark. 
Pan-sharpened  products  combine  the  visual  coloured  information  of  the  Multispectral  data  with  the  details 
provided by the panchromatic data, resulting in a higher resolution 0.3m colour product.  
Typically, three or four low-resolution visible bands – blue, green and red or green, red and near infrared – are 
used as the main inputs in the process to produce a very high-resolution natural colour or false colour image.  
For Pan-sharpened products, Airbus uses its proprietary fusion processing. Performing its own pan-sharpening 
is a fine process.  
Pan-sharpened products are offered as three, four and six-band products:  
PMS_N: Pansharpening in natural colour (3 bands RGB)  
PMS_X: Pansharpening in false colour (3 bands NIRRG)  
PMS: Pansharpening (4 bands RGB+NIR)  
Full PMS: Pansharpening (6 bands)  
The Pléiades Neo constellation offers a wide range of products and services featuring different 
options to match as closely as possible to any customer requirements. 

 
 
 
 
 

Table 4. Standard product options delivered by Image Production Facility 

Geometric processing options  

Radiometric processing options  

Imagery format  

Product encoding  

Spectral combinations  

Primary  
Projected  
Ortho  
Basic  
Reflectance  
Display  
JPEG 2000 Optimised  
JPEG 2000 Regular  
GeoTIFF  
12 bits  
8 bits  
PAN: Panchromatic (1 band)  
Bundle: PAN + MS (4 bands RGB+NIR)  
Full Bundle: PAN + Full MS (6 bands)  
MS: Multispectral (4 bands RGB+NIR)  
Full MS: Multispectral (6 bands)  
PMS_N: Pansharpening in natural colour (3 bands RGB)  
PMS_X: Pansharpening in false colour (3 bands RGNIR)  
PMS: Pansharpening (4 bands RGB+NIR)  
Full PMS: Pansharpening (6 bands)  

Source: Airbus 

6 

 
 
 
 
3.2  Geometric Processing Levels 

Pléiades  Neo  core  imagery  products  are  available  in  three  different  geometric  processing  levels:  Primary, 
Projected and Ortho.  

All Pléiades Neo products are corrected for non-uniformity sensor radiometric and distortions, using internal 
calibration parameters, ephemeris and attitude measurements.  

Standard products offer the Panchromatic channel (product resolution: 0.3m) or the Multispectral channels (six 
bands, product resolution: 1.2m) already registered and possibly merged. 

Primary products: 

This Primary product corresponds to that of the products supplied as input by Airbus in order to 
carry out the various orthorectification tests of this Benchmark. 

The Primary product is the geometric processing level closest to the source image acquired by the sensor. This 
product restores perfect collection conditions: the sensor is placed in rectilinear geometry, and the image is 
cleared  of all  radiometric distortion. This  product is optimal for  those  clients  familiar  with  satellite imagery 
processing techniques who want to apply their own production methods (orthorectification or 3D modelling for 
example).  To  this  end,  Rational  Polynomial  Coefficients  (RPCs)  and  the  sensor  model  are  provided  with  the 
product to ensure full autonomy and simplicity for users. The Primary level product is:  

 

In sensor geometry, synthesised on a perfect single and linear push-broom array.  

  With an equalised radiometry on the native dynamic range of the sensor, 12 bits (4096 values).  

The  product is  extracted  from one  strip acquisition. The  support for  this extraction is a  polygonal  region of 
interest in WGS84 coordinates.  

The main geometric processing includes:  

The combination of all sub-swaths across the field of view (14km nadir condition): synthesis in a virtual focal 
plane represented by a single linear array for all spectral bands.  

Correction of instrumental and optical distortions: viewing angles adjusted to the single linear array model.  

Co-registration of all spectral bands: Multispectral and Panchromatic.  

Attitudes and ephemeris data are refined at ground on the mean estimation:  

  Adjustment of the time stamp sampling (along scan line).  

  Attitudes filtering over time of acquisition.  

Consistent alignment of the physical location model ancillary data and RPC analytic model data.  

When appropriate the image location can be improved by Ground Control Points from the Airbus Intelligence 
worldwide Space Reference Points database.  

The main radiometric corrections/enhancements include:  

Inter-detector equalisation: correction of differences in sensitivity between the detectors (on-board correction).  

Aberrant detectors correction (none at that time).  

Panchromatic band restored and de-noised. 

Orthoimages: 

The  description  of  the  orthorectification  process  below  concerns  the  9  tests  performed  on 
orthorectified (Ortho) products from standard Airbus lines. 

The Ortho product is a georeferenced image in Earth geometry, corrected from acquisition and terrain off-nadir 
effects. The Ortho is produced as a standard, with fully automatic processing.  

The standard Ortho product is an image that has been corrected (viewing angle and ground effects) so that it 
can be superimposed on a map. In addition to radiometric and geometric adjustments, a geometric process 

7 

 
 
 
using  a  relief  model  (known  as  orthorectification)  eliminates  the  perspective  effect  on  the  ground  (not  on 
buildings), restoring the geometry of a vertical shot. The Ortho product is optimal for simple and direct use of 
the image. It can be used and ingested directly into a Geographic Information System.  

This processing level facilitates the management of several layers of products, from the same sensor or others, 
while reducing localisation gaps that can be caused by different viewing angles or relief between the various 
layers. To improve the accuracy of the product, Airbus uses its own worldwide 3D reference layers to perform 
ground corrections: Space Reference Point Ground Control Points (GCP) and WorldDEM4Ortho hybrid DTM/DSM 
for orthorectification.  

The product is extracted from one to several contiguous strip acquisitions: single ortho or mosaic. Support for 
this extraction is a polygonal region of interest in WGS84 coordinates.  

The  Ortho  level  product  inherits  the  same  geometric  corrections  from  the  Primary  level,  with  additional 
adjustments: 

Planimetric reset: On request, if ground reference data is available, the location is reset on Ground. By default, 
Space Reference Points are used, otherwise PAS ortho tiles are used where available.  

Altimetric reset: with a Digital Elevation Model (DEM) to correct acquisition angles and relief off-nadir effects 
(as a vertical shot) for the ortho-rectification resampling process. By default, the WorldDEM4Ortho layer is used 
where available, otherwise Reference3D DEM or Shuttle Radar Topography Mission (SRTM) DEM is used.  

Map projection or geographic projection.  

The  Ortho  product  inherits  same  radiometric  corrections  and  enhancements  from  the  Primary  or  Projected 
processing level, with additional adjustments:  

Pixel  sampling  (Spline  kernel)  at  a  regular  resolution  of  1.2m  for  Multispectral  products  and  0.3m  for 
Panchromatic and Pan-sharpened products.  

8 

 
4  Study Area 

The  test  AOI  is  located  in  the  commune  Maussane-les-Alpilles  in  the  Provence-Alpes-Cote  d’Azur  Region  in 
southern  France  and  is  being  used  as  a  ‘test  site’  by  the  European  Commission  since  1997.  The  AOI  is 
characterized by different land use types and the terrain variations (high difference between highest and lowest 
point is around 300m). The area used in the tests is 100km2 and spans 4◦41’ to 4◦48’E and 43◦40’ to 43◦45’N 
(Figure 1) 

Figure 1. Location of the test site and its AOI 

Source: JRC adapted from Google map information 

9 

 
 
 
5  Auxiliary data and imagery used 

5.1  Ground Control Points 

Ground Control Points play an important role in orthorectification process of satellite imagery aiming at 
improving the planimetric accuracy of created ortho images. These points cannot be randomly selected and 
the general principles for GCPs’ selection would be as follows: 

 
 
 
 
 

should represent a prominent feature; 
should be well identified features; 
should be well identified in the image; 
should be well distributed; 
and objects that represent vertical displacements should not be used. 

In  addition,  Guidelines  for  Best  Practice  and  Quality  Checking  of  Ortho  Imagery  [i]  specifies  the  accuracy 
requirements for GCPs i.e. 

‘’GCPs should be at least 3 times (5 times recommended) more precise than the target specification for the 
ortho, e.g. in the case of a target 2.5m RMSE, the GCPs should have a specification of 0.8m RMSE or better’’. 

According to VHR Image Acquisition Specifications for the CAP checks (CwRS and LPISQA) - VHR profile-based, 
target ortho image accuracy for VHR prime is 2m/1.5m/1.25m and 5m for VHR Backup [ii]. 

Considering  all  the  above,  set  of  9  GCPs  (Table  5,  Figure  2)  to  be  used  in  the  modelling  phase  in  the 
orthorectification process of six Pléiades NEO imageries has been selected from GCP dataset received from 
JRC. 

Table 5. Ground Control Points available for the Maussane test site 

Dataset  

ADS40_GCP_dataset_Maussane_ 
prepared_for_ADS40_in_2003 

VEXCEL_GCP_dataset_Maussane_  
prepared_for_VEXEL_in_2005 
Multi-use_GCP_dataset_Maussane_  
prepared_for_multi-use_in_Oct-2009 
Cartosat-1_GCP_dataset_Maussane_prepared_  
for_Cartosat_in_2006 
Formosat-2_GCP_dataset_Maussane_  
prepared_for_Formosat2_in_2007  
Cartosat-2_GCP_dataset_Maussane_  
prepared_for_Cartosat-2_in_2009  
SPOT_GCP_dataset_Maussane_  
prepared_for_SPOT_in_  

Maussane GNSS field campaign 
21-26 November 2012 

Point ID  

RMSEx [m]  

RMSEy [m]  

11XXXX 

0,05 

44XXXX 

66XXX 

33XXX 

7XXX 

55XXX 

22XXX 

0,49 

0,30 

0,55 

0,88 

0,90 

n/a 

0,10 

0,50 

0,30 

0,37 

0,72 

0,76 

n/a 

CXRX 

0,15 

0,15 

Source: JRC 

The GCPs were chosen from a set of existing collections of some 55 points in the area covering the Maussane 
site. 

10 

 
 
 
 
 
 
 
Figure 2. Distribution of GCPs available in the AOI, the background image is an elevation map of the area. 

Source: JRC 

GCPs Selection for orthorectification 

A first selection of 9 GCPs was made in order to orthorectify the primary images according to the parameters 
provided in the different tests. 

3 dataset have been used for this selection, 5 GCPs in MAUSS-2009, 3 GCPs for the dataset VEXEL and d 1 GCP 
selected in the dataset ADS40. The worst accuracy is 0.49m RMSE in x and 0.5m RMSE in y but still meeting 
the required specification. "GCPs [and ICPs] should be at least 3 times (5 times recommended) more precise 
than the target specification for the ortho, e.g. in the case of a target RSME1D _ 2:5m, the GCPs [and ICPs] 
should have a specification of RSME1D <= 0:8m." 

Table 6. Collection used for the 9 GCPs selected for the orthorectification process 

Dataset  

Point ID  

RMSEx [m]  

RMSEy [m]  

ADS40_GCP_dataset_Maussane_ 

prepared_for_ADS40_in_2003 

VEXCEL_GCP_dataset_Maussane_  

prepared_for_VEXEL_in_2005 

Multi-use_GCP_dataset_Maussane_  

prepared_for_multi-use_in_Oct-2009 

11XXXX 

0,05 

0,10 

44XXXX 

0,49 

0,50 

66XXX 

0,30 

0,30 

Number 
of  GCPS 
selected 

1 

3 

5 

Source: JRC 

11 

 
 
 
 
Figure 3. Distribution of 9 GCPs selected for orthorectification process, the background image represents an elevation 
map of the area and the red square corresponding to the study AOI. 

GCPs Selection for internal control 

Source: JRC 

Another set of 9 control points has been selected allowing independent internal control of the orthoimages to 
ensure their quality.  

2 dataset have been used for this selection 4 GCPs in MAUSS-2009 and 5 GCPs for the dataset VEXEL. Like for 
the first selection the worst accuracy is 0.49m RMSE in x and 0.5m RMSE in y. These accuracies are meeting 
the required specifications. 

Table 7. Collection used for the 9 GCPs selected for the internal control 

Dataset  

Point ID  

RMSEx [m]  

RMSEy [m]  

VEXCEL_GCP_dataset_Maussane_  

prepared_for_VEXEL_in_2005 

Multi-use_GCP_dataset_Maussane_  

prepared_for_multi-use_in_Oct-2009 

44XXXX 

0,49 

0,50 

66XXX 

0,30 

0,30 

Number 
of  GCPS 
selected 

5 

4 

Source: JRC 

12 

 
 
Figure 4. Distribution of 9 GCPs selected for internal control, the background image represents an elevation map of the 
area and the red square corresponding to the study AOI 

The selection of GCPs was done to ensure a good distribution of points over the entire area trying to follow the 
model of the 3 _ 3 spatial pattern of von Gruber points (like a 6 on a dice). However, in the north of the area 
only one point was found usable due to the complex relief. 

Source: JRC 

Comments about the GCPs selection:  

Even if a sufficient number and a good distribution of points were finally obtained, various problems have been 
identified during their selection: 

 

 

 

The last GCPs collection dates from at least 13 years. The landscape may have changed significantly 
in some locations, so the points recorded in these areas are no more usable. 
The location of some points is not enough precise, centre of crossroads for example, to allow for a 
good repositioning. 
The points taken on elements presenting a significant elevation which are difficult to find on the 
acquisitions presenting a strong angle (electric pole or wall of more than 2 m). 

Even if it is difficult to quantify, the elements listed above may lead to a significant pointing error of the GCPs 
that we estimate being around 1 meter. 

13 

 
 
 
 
 
5.2  Digital Elevation Model (DEM) 

A DEM is used in order to correct image displacement caused by topographic relief. Therefore, used DEM should 
have an accuracy compatible with the resolution of VHR to be corrected.  

Thus,  as  reported  in  the  Guidelines  for  Best  Practice  and  Quality  Checking  of  Ortho  Imagery  [i],  the 
recommendations are to use DEM: 

  with maximum grid spacing of 5 to maximum 20 times the orthophoto pixel size (depending on the 

terrain flatness) and  

  with height accuracy of 2 x planimetric 1-D RMSE 

Figure 5. Relief of the study AOI 

Source: JRC out of Intermap Company DEM data 

The  DEM  used in these tests was  produced  by  the Intermap  company from Synthetic  Aperture  Radar  (SAR) 
airborne shots using interferometry method. 

The grid resolution of this DEM is 5M. We chose to use the DTM to avoid all the othorectification artefacts linked 
to the details of the DEM (tree buildings, etc.) and to the strong viewing angles which could have hindered the 
detection of the points in the image. 

To note that a DEM should not be confused with a Digital Terrain Model (DTM) that is a topographic model of 
the bare earth that has vegetation, buildings, and other cultural features  digitally removed, leaving just the 
underlying terrain.” (Intermap citation). 

The DEM grid spacing and DEM height accuracy are within the requested specifications. 

14 

 
 
 
 
Table 8. ORI and CORI accuracy specifications 

Pixel size 

RMSE1 

CE952 

1.25 m 

2.0 m 

4.0 m 

Source: JRC 

Table 9. DTM vertical accuracy specifications 

DTM 

Measures of Accuracy 

Specifications 

Product Type 

RMSE 

CE95 

Pixel Size / Post 
Spacing 

I 

II 

0.7 m 

1.5 m 

1.0 m 

2.0 m 

Source: JRC 

5 m 

5 m 

One has to note that the data accompanying the DEM suggested that it was already at the ellipsoid. However, 
the first attempts at orthorectification on the ERDAS software ended in failure. After investigation by the Airbus 
production team, it appeared that the DTM was on the Geoid. A first step therefore consisted of referencing the 
altitudes on the ellipsoid thanks to the Pixel Factory system. 

5.3  Aerial Orthomosaics used 

Here below in Table 10 are listed the details of the aerial Orthomosaics used for the work. 

Table 10. Aerial Orthomosaics Specifications 

Aerial 
Orthomosaics 

Grid size 

Accuracy 

Projection 
and datum 

Source 

ADS40 

0,5m 

n/a 

UTM  31N 
WGS84 

ADS40  aerial  flight  by  ISTAR,  2003. 
Bands: R, G, B, IR, PAN 

Vexel UltraCam 

0,5m 

n/a 

flight  by 
Vexel  Ultracam  aerial 
Aerodata, 2005. Bands: R, G, B, IR, PAN 

5.4  Pléiades Neo satellite imagery used 

Source: JRC 

To perform these benchmark test, 3 images over Maussane AOI have been acquired by each sensor with three 
configuration of acquisition to test the orthorectification process. 

PNEO3 

  NADIR acquisition with an angle of maximum 5° 19/05/2022 
  MEDIUM acquisition with an angle of maximum 20° 20/02/2022 
  HIGHT acquisition with an angle of maximum 30° 22/03/2022 

PNEO4 

  NADIR acquisition with an angle of maximum 5° 17/02/2022 
  MEDIUM acquisition with an angle of maximum 20° 01/02/2022 
  HIGHT acquisition with an angle of maximum 30° 11/02/2022 

15 

 
 
 
 
 
 
 
 
 
Table 11. Real angles of the primary product across study AOI 

Sensor  Acquisition 
guidance 

Acquisition 
constraints 

Date 

Along 

Across  Viewing 

PNeo3  NADIR 
PNeo3  MEDIUM 
PNeo3  HIGHT 
PNeo4  NADIR 
PNeo4  MEDIUM 
PNeo4  HIGHT 

5 
20 
30 
5 
20 
30 

19/05/2022 
20/02/2022 
22/03/2022 
17/02/2022 
01/02/2022 
11/02/2022 

Source: Airbus 

-3.7 
-7.9 
-4.5 
-3.8 
3.6 
-7.6 

-1.3 
11.6 
-25.9 
-1.3 
-14.4 
-25.8 

3.9 
13.9 
26.2 
3.9 
14.8 
26.7 

Acquisitions took  place from the beginning of February 2022  until May 2022. It is noted that the restricted 
angles requested put constraints in the acquisition attempts. Also the weather has not been favourable in spring, 
so the PNeo3 image at nadir could only be acquired late. 
In the end, the radiometric conditions are generally good for all the acquisitions but some contain some clouds. 
Each acquisition was then produced with these parameters: 

Surface: limit of the AOI of 100km²; 

 
  Geometric processing: level sensor (primary); 
 
 
 

Spectral Bands combination: Pansharpened 6 bands; 
Product format: DIMAP Regular JPEG 2000; 
Pixel coding: 12 bits with basic level. 

We made the choice of jpeg 2000 format to limit the size of each product (about 4.8GB). 
These products have been ordered on the OneAtlas platform and produced with automatic chain of 
production. 

Figure 6. Interface OneAtlas with order parameters 

Source: Airbus 

16 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 7. View in natural colour (RGB) and false colour (NED) of one acquired scene 

RGB 

NED 

Source: Airbus 

Note that the final product consists of 2 image files containing the visible RGB bands (RED, GREEN, BLUE) for 
the first and the NED bands (Near Infrared, Red Edge, Deep Blue) for the second. Each of these images is in 
turn tiled in order to limit the size of the image files. 
For user-friendly use, it is preferable to set the processing software to open the images via their DIMAP file so 
that it can automatically manage the tiling as well as the 6 bands. 

5.5  Software 

To be able to carry out the orthorectifications within a short time, the choice of software was made according 
to the licenses available within Airbus Geo as well as the know-how present in the teams. 

From the JRC work, both ENVI and Erdas imagine software were used. This choice is made since these softwares 
are the most commonly used in the image processing community. 

●  ENVI:  

From the information collected under https://www.l3harrisgeospatial.com/Software-Technology/ENVI, “ENVI is 
an industry standard for image processing and analysis software. It is used by image analysts, GIS professionals 
and  scientists  to  extract  timely,  reliable  and  accurate  information  from  geospatial  imagery.  This  geospatial 
software is scientifically proven, easy to use and tightly integrated with Esri’s ArcGIS platform.” 

“ENVI image analysis software has remained on the cutting edge of innovation for more than three decades 
due in part to its support of all types of data including multispectral, hyperspectral, thermal, LiDAR and SAR. 
ENVI  makes  deep  learning  accessible  to  people  through  intuitive  tools  and  workflows  that  don’t  require 
programming. ENVI geospatial image analysis can also be customized through an API and visual programming 
environment to meet specific project requirements.”  

We used version 5.6.1 accompanied by a patch to import the PNeo product via the DIMAP format. 

  The point positioning process is quite intuitive and easy. 
 

It was not possible to reuse points already exported to facilitate positioning and obtain time savings in 
the realization of orthoimages. 
It is not possible to integrate GCPs under a cartographic projection, the points have been previously 
converted into geographical coordinates (wgs84). 

 

17 

 
 
 
 
 
  When  processing  the  orthoimage,  we  were  faced  with  processing  stops  without  an  error  message. 
Parametrization tests in the calculation processing options have not been conclusive. The problem was 
solved by changing the hardware with newer technology RAM. 

  The choices of export format are few. Only the ENVI internal format and the Geotiff are available. This 
forced us to carry out an additional conversion in order to export the products in JPEG2000, which is 
less bulky and easier to handle. 

  Finally, the PNeo physical model was not ready to be integrated into the latest version of the distributed 
software.  A  solution  was  found  thanks  to  the  Harris  Company  which  gave  us  access  to  its  service 
provider in order to find a solution to produce the necessary ortho images with the rigorous model. 

●  Erdas Imagine: 

Imagine 

Erdas 
(https://hexagon.com/products/erdas-imagine) 

is  an 

image  processing  software  and  a  GIS  distributed  by  Hexagon  company 

”ERDAS  IMAGINE  provides  true  value,  consolidating  remote  sensing,  photogrammetry,  LiDAR  analysis,  basic 
vector analysis, and radar processing into a single product.”  

We used version 2022 of Erdas Imagine accompanied by a patch to import the PNEO product via the DIMAP 
format. 

The  older  interface  of  ERDAS  was  more  complicated  to  understand  but  ultimately  offers  more  ease  of 
adaptation to its working environment. 

18 

 
 
6  Pléiades Neo Benchmarking Tests 

6.1  Benchmarking Methodology 

Orthorectification of images is a process to apply corrections for optical distortions from the sensor system, 
and apparent changes in the position of ground objects caused by the perspective of the sensor view angle and 
ground terrain. This process usually consists of 3 phases: 

Phase 1: Modelling - geometric correction model phase, also referred as to image correction phase, sensor 
orientation phase, space resection or bundle adjustment  phase. Sensor models are mathematical models 
that  define  the  physical  relationship  between  image  coordinates  and  ground  coordinates,  and  they  are 
different for each  sensor.  In  this  phase Ground Control  Points  are  used  for  improving absolute accuracy. 
However, the tests were also performed without using GCPs. 

Phase 2: Orthorectification - the phase where distortions in image geometry caused by the combined effect 
of terrain elevation variations and non-vertical angles from the satellite to each point in the image at the 
time of acquisition are corrected. 

Phase 3: External Quality Control (EQC) of the final product - described by 1-D RMSEx and 1-D RMSEy – 
performed  by  JRC.  According  to  Guidelines  for  Best  Practice  and  Quality  Checking  of  Ortho  Imagery  [i] 
minimum 20 check points should be checked to assess ortho image planimetric accuracy. The points used 
during the geometric correction phase should be excluded. 

Figure 8. Standard benchmarking procedure 

Source: JRC 

6.2  Test Scenarios 

The following scenarios have been considered in our benchmarking tests: 

Table 12. Tested Scenarios 

IMAGES 

DEM 

GCP #  model 

ERDAS 

ENVI 

PNeo3 

PNeo4 

TOTAL 

PNeo3/4  close 
nadir 

Intermap 

PNeo3/4  close 
nadir 

Intermap 

0 

3 

4 

6 

RPC (0) 

Rigorous 

1 

1 

1 

1 

19 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

4 

4 

4 

4 

 
 
  
PNeo3/4 
medium 

Intermap 

PNeo3/4 
medium 

Intermap 

PNEO3/04 high 

Intermap 

PNEO3/04 high 

Intermap 

Total 

0 

3 

4 

6 

0 

3 

4 

6 

6 

9 

RPC (0) 

Rigorous 

RPC(0) 

Rigorous 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

4 

4 

4 

4 

4 

4 

4 

4 

4 

4 

56 

IMAGES 

DEM 

angle 

model 

ENVI 

PNeo3 

PNeo4 

TOTAL 

PNeo3/4 

Airbus 
DEM 

low 

medium 

high 

automatic 
ortho 
by 
Airbus  (no 
GCPs) 

1 

1 

1 

1 

1 

1 

1 

1 

1 

 Total 

Source: JRC 

2 

2 

2 

6 

62 test products were carried out with the 6 images equally between the PNeo3 and PNeo4 sensors.  

The organization of work is carried out in 7 batches even if foreseen order could not be respected depending 
on the availability of acquisitions or software. 

The same tests were carried out for each sensor.  

Acquisitions with a high angle were tested more than acquisitions at nadir and medium acquisition angle. It 
represents 42% of the tests against 29% for each of the other 2 types of medium and nadir angles. 

The number of tests done with ENVI and ERDAS is the same. Note that the 6 tests done on the airbus chain, 
whose production is in automatic mode, could not allow following the same test processes as for ENVI and 
ERDAS software. 

Regarding the GCPs, 5 test configurations were performed with 0, 3, 4, 6 and 9 GCPs. The configuration with 6 
GCPS was the most tested 26% and that with 9 GCPs the least tested 6%. The configurations with 0, 3 and 4 
GCPs have the same number of tests and each represents 19% of the tests. Note the presence of 6 Airbus 
products which have been produced in automatic mode. 

Finally, 2 modelling  modes were used. The first, RPC, represents  65% of the tests and the second, rigorous 
modelling, represents 16% of the tests. 

20 

 
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
Table 13. Distribution of tests according to parameters 

test number 

total by parameter 

sensor 

PNeo3 

PNeo4 

acquisition angle  

low 

medium 

hight 

software 

Airbus chain 

GCP number 

ENVI 

ERDAS 

0 

3 

4 

6 

9 

Auto (airbus) 

model 

RPC 

rigourous 

AirbusDS 

31 

31 

18 

18 

26 

6 

28 

28 

12 

12 

12 

16 

4 

6 

40 

16 

6 

62 

62 

62 

62 

62 

6.3 

Internal Quality Control (Airbus) 

Source: JRC 

The modelling results provided by each software were checked during each orthorectification process. 

However, the difficulty of exporting these results during orthorectification did not make it possible to have an 
overview and a comparison of these results. 

Airbus has implemented an internal control to check the quality of: 

 
statement of the coordinates of 10 independent GCPs on all the orthorectifications; 
  measurement of difference in x and y with respect to the initial coordinates of each GCP; 
 

calculation of the RMSEx and the RMSEy. 

21 

 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
 
Table 14. Acquired Images 

Source: Airbus 

Only 3 images (out of 62) exceed the threshold of 1.25 for the RMSEx and the RMSEy and another 4 other 
images only exceeding the threshold of 1.25 RMSEx. 

Of these 7 images, 2 are part of the 6 orthorectification products carried out on the automatic and standard 
Airbus processing chain. 

As mentioned previously, we consider that the pointing error of some GCPs can reach 1 meter. This pointing 
error can affect both on the GCPs used for the orthorectification product of the images as well as on those used 
for internal and external control. This aspect must therefore be taken into account when reading these results. 
Also, the Airbus processing chain was still under development and not finalised at the time of the benchmarking 
study. 

Impact of sensors 

Figure 9. RMSEx and RMSEy depending on the sensor 

On Figure 9 we can note similar results for the 2 sensors if even the results are slightly better for PNeo3 in y 
0,58m versus 0,7m7 for PNeo4. 

Source: Airbus 

22 

 
 
 
 
 
 
 
Impact of acquisition angle 

Figure 10. RMSEx and RMSEy depending on the acquisition angle 

Source: Airbus 

As expected the test shows that the distribution of RMEx and RMSEy errors is increasing while getting off nadir 
acquisitions.  Some  points  are  out  of  threshold  accuracy  in  the  medium  and  high  acquisition  angles.  This 
phenomenon should be monitored in the future. 

Impact of models (RPC or rigorous) 

Figure 11. RMSEx and RMSEy depending on the orthorectification model used 

Source: Airbus 

23 

 
 
 
 
 
The images orthorectified via the rigorous model are of better quality. The difference between rigorous model 
and RPC is very noticeable in the direction of axis y. However, the reason for this difference was not further 
investigated since it was not part of the scope of the benchmark work. 

Impact of GCPs number 

Figure 12. RMSEx and RMSEy depending on the number of GCPs used 

Without GCPs the RMSEx and RMSEy are close to 1.1m. 

Source: Airbus 

As expected, the use of GCPs allows improving the positional accuracy. However, with the set of GCPs available, 
when further increasing their number, we observed an improvement of the localization in y, but a deterioration 
of the localization in x. Since it is not part of the main scope of the benchmarking tests, this has not been further 
investigated to possibly identify the reason(s) of this behaviour. 

Impact of Software 

Figure 13. RMSEx and RMSEy depending on the number of GCPs used 

 Source: Airbus 

24 

 
 
 
The difference between the ortho products of ERDAS Imagine and ENVI are minimal. Airbus software provides 
less accurate results since their automatic processing chain in place does not use GCPs. 

To check the proper performance of the 3 softwares, a comparison should be done with results obtained with 
no GCPs used. Due to lack of time this comparison has not been done. 

25 

 
7  External Quality Control 

JRC as an independent entity performs a validation phase of the benchmarking workflow methodology used for 
verifying of a satellite’s ortho-product compliance with the geometric quality criteria set up for the Control with 
Remote Sensing program (CwRS), in Common Agriculture Policy (CAP). The workflow follows the Guidelines for 
Best Practice and Quality Checking of Ortho Imagery [i] and is in detail described in the chapter 6.1. 

7.1  Method for external quality check (EQC) of ortho imagery 

7.1.1 

Independent check points (ICPs) selection and distribution 

For  the  evaluation  of  the  geometric  accuracy  of  the  PNeo  ortho  imagery,  22  ICPs  were  selected  by  a  JRC 
operator. Both GCPs and ICPs were retrieved from already existing datasets of differential global positioning 
system (DGPS) measurements over Maussane test site. These datasets were updated and maintained by JRC 
until 2012. Considering the accuracy, distribution and recognisability on the given images, points from the three 
datasets were used for the EQC. The intention is to obtain points evenly distributed across the whole image 
while keeping at least the minimum recommended number of 20 points [i]. For the location of the ICPs, JRC 
took into account the distribution of the GCPs used by Airbus and provided to JRC together with the products. 
Since the measurements on ICPs have to be completely independent (i.e. ICP must not correspond to GCP used 
for  correction)  GCPs  taken  into  account  in  the  geometric  correction  have  been  excluded  from  the  datasets 
considered for EQC. 

Regarding the positional accuracy of ICPs, according to the Guidelines [i] the ICPs should be at least 3 times (5 
times recommended) more precise than the target specification for the ortho, i.e. in our case of a target 1.25m 
RMS error (the most strict value was taken into account here) the ICPs should have a specification of 0.42m 
(0.25m recommended). 21 ICPs that have been selected fulfil the defined criteria and 1 ICP is slightly above 
the mentioned threshold (Table 15, Table 16). 

Table 15. Identical check points specifications 

Dataset 

RMSEx [m] 

RMSEy [m] 

N.of points  

ADS40_GCP_dataset_Maussane 2003 

VEXEL_GCP_dataset_Maussane 2005 

Multi-use_GCP_dataset_Maussane 2009 

0,05 

0,49 

0,30 

0,10 

0,50 

0,30 

Maussane GNSS field campaign 2012 

< 0,15 

< 0,15 

Source: JRC 

1 

1 

15 

5 

26 

 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 14. ICPs dataset used by JRC in the EQC of PNeo ortho imagery. 

Source: JRC with Airbus PNeo background image 

Table 16. ICPs overview 

ID 

E[m] 

N[m] 

C4R5NEW  64507924.00  484001539.00 

C4R4 

64531764.00  484323364.00 

110013 

639418.26 

4840602.35 

66063 

636896.93 

4842180.72 

66046 

641148.67 

4837348.79 

C2R4 

63782972.00  484360987.00 

C3R5NEW  64034136.00  483888755.00 

C3R4 

64160872.00  484312915.00 

66010 

643598.10 

4845690.29 

66004 

636363.62 

4846077.52 

66020 

637261.09 

4837987.96 

66022 

637947.95 

4837300.70 

66014 

645687.64 

4845487.95 

27 

 
 
66011 

644844.93 

4844945.47 

66016 

636347.01 

4837279.93 

440003 

640999.13 

4845715.57 

66031 

644655.96 

4839947.67 

66025 

641380.52 

4841215.07 

66024 

641320.70 

4838276.56 

66026 

640049.05 

4840996.07 

66035 

644717.26 

4837489.03 

Source: JRC 

The projection and datum details of the above mentioned data are UTM 31N zone, WGS 84 ellipsoid. 

7.1.2  Geometric quality assessment-measurements and calculations 

Geometric  characteristics  of  orthorectified  images  are  described  by  Root-Mean-Square  Error  (RMSE)  RMSEx 
(easting direction), RMSEy (northing direction) and CE(90), calculated for a set of Independent Check Points.  

RMSE

RMSE

x

y



R

MSE

1D

(

East

)



1
n

n



i

1




X

REG

)(
i



X

)(
i

2





R

MSE

1D

(

North

)



1
n

n



i

1



Y

REG

)(
i



Y

)(
i

2



where  X,YREG(i)    are  ortho  imagery  derived  coordinates,  X,Y(i)    are  the  ground  true  coordinates,  n 

express the overall number of ICPs used for the validation. 

to  as 
This  geometric  accuracy 
planimetric/horizontal accuracy and it is based on measuring the residuals between coordinates detected on 
the ortho image and the ones measured in the field or on a map of an appropriate accuracy. 

the  positional  accuracy,  also 

representation 

is  called 

referred 

According to ISO 19157, the circular error at 90% CE(90) significant level (or confidence interval) is defined as 
a radius describing a circle, in which the true point location lies with the probability of 90 %. It is also known as 
CMAS (circular map accuracy standard). 

)90(CE



2,146

RMSE

(

East

)

2



RMSE

(

North

)

2

2

If the error is normally distributed in each the x- and y-component, the error for the x-component is 
equal to and independent of error for the y-component, and sufficient check points are available to accurately 
estimate the variances, CE90 can be expressed as 2,146 times the one dimensional root mean square error: 

)90(CE



2,146



RMSE
(

East

)

  or  

)90(CE



2,146



RMSE

(

North

)

Unlike the values obtained from the field measurements (in our case with GPS device) which are of the defined 
accuracy the coordinates registered from the involved ortho images are biased by various influencing factors 
(errors of the source image, quality of auxiliary reference data, visual quality of the image, experience of an 
operator  etc..).  It  should  be  taken into account that all these  factors are  then  subsequently  reflected in the 
overall RMSE which in practice aggregates the residuals into a single measure. 

28 

 
 
 
 
 
 
 
 
 
 
 
 
All  measurements  presented  were  carried  out  in  ERDAS  Imagine  2016  software,  using  Metric  Accuracy 
Assessment.  Protocols  from  the  measurements  contain  other  additional  indexes  like  mean  errors  or  error 
standard deviation that can also eventually help to better describe the spatial variation of errors or to identify 
potential systematic discrepancies [i]. 

Since the JRC datasets of DGPS points are of a high variety as for the date of origin is concerned (2003-2012) 
many points were difficult to detect due to the meanwhile change of the overall landscape. Also, the ADS40 
aerial orthomosaic is 19 years old and therefore does not always correspond to the actual state of the region. 
Thus, for the selection of some ICPs on the ortho images the other complementary sources to the aerial image 
were used, like for instance previously orthorectified VHR images or Google Earth 2D sequences, which helps to 
follow the change of the situation during the years, for some cases (where available) also 3D view. 

Due to the fact that JRC datasets are obsolete (i.e. some GCPs/ICPs are difficult to identify, an example is point 
66007 which is not included in the analysis due to very high discrepancies) the results may be encumbered 
with additional errors. 

29 

 
 
8  Results and discussions 

Results of the quality control are presented hereafter under different forms and formats in order to illustrate 
and discuss the possible impact of factors intervening in the final positional accuracy such as the sensors, the 
GCPs, the acquisition angle, the image processing software or even the ortho correction model. 

8.1 

Impact of the orthorectification model 

In Table 17 and Table 18 are provided the results of the QC using the Rational Function modelling for the 
PNeo3 and PNeo4 sensors respectively. 

Table 17. Obtained quality control results (RMSE1D) on ortho image produced by applying Rational Function Modelling, 
using JRC ICPs dataset – PNeo3 

ONA 

(Off 
Nadir 
Angle) 

5˚ 

20˚ 

30˚ 

RPC 
model 

GCPs 

0 

3 

4 

0 

3 

4 

0 

3 

4 

6 

ERDAS 

ENVI 

RMSEx 
ERDAS 

RMSEy 
ERDAS 

RMSEx 
ENVI 

RMSEy 
ENVI 

0.81 

0.89 

0.75 

1.99 

0.78 

0.72 

1.03 

1.27 

1.06 

1.01 

1.02 

0.82 

0.78 

1.40 

0.84 

0.72 

0.99 

0.74 

0.73 

0.63 

0.79 

0.88 

0.82 

1.92 

0.94 

0.99 

1.12 

1.30 

1.34 

1.68 

0.95 

1.05 

0.89 

1.48 

0.67 

0.59 

0.87 

0.60 

0.68 

0.65 

Table 18. Obtained quality control results (RMSE1D) on ortho image produced by applying Rational Function Modelling, 
using JRC ICPs dataset – PNeo4 

Source: JRC 

ONA 

RPC model 

ERDAS 

ENVI 

GCPs 

RMSEx 
ERDAS 

RMSEy 
ERDAS 

RMSEx 
ENVI 

RMSEy 
ENVI 

5˚ 

0 

3 

4 

0.70 

0.58 

0.66 

0.78 

0.70 

0.77 

0.56 

0.87 

0.98 

0.80 

0.76 

0.55 

30 

 
20˚ 

30˚ 

0 

3 

4 

0 

3 

4 

6 

1.32 

1.13 

1.03 

1.69 

1.23 

1.01 

0.97 

1.11 

0.90 

0.87 

1.26 

0.98 

0.73 

0.74 

Source: JRC 

1.52 

0.98 

1.08 

1.87 

1.16 

1.45 

1.39 

1.28 

0.72 

0.84 

1.96 

0.92 

0.80 

0.82 

In Table 19 and Table  20 are provided the results of the QC using the Rigorous model for the PNeo3 and 
PNeo4 sensors respectively. 

Table 19. Obtained quality control results (RMSE1D) on ortho image produced by applying Rigorous Modelling, using JRC 
ICPs dataset – PNeo3 

Rigorous 
model 

ERDAS 

ENVI 

ONA 

GCPs 

RMSEx 
ERDAS 

RMSEy 
ERDAS 

RMSEx 
ENVI 

RMSEy 
ENVI 

5 

20 

30 

6 

6 

6 

9 

0.76 

0.86 

0.90 

1.01 

0.69 

0.74 

0.67 

0.69 

0.71 

0.77 

0.68 

1.13 

0.74 

0.76 

0.64 

0.66 

Source: JRC 

Table 20. Obtained quality control results (RMSE1D) on ortho image produced by applying Rigorous Modelling, using JRC 
ICPs dataset – PNeo4 

Rigorous 
model 

ERDAS 

ENVI 

ONA 

GCPs 

RMSEx 
ERDAS 

RMSEy 
ERDAS 

RMSEx 
ENVI 

RMSEy 
ENVI 

5 

20 

30 

6 

6 

6 

9 

0.81 

1.21 

1.27 

1.25 

0.71 

0.77 

0.85 

0.83 

0.89 

0.99 

1.32 

1.38 

0.73 

0.79 

0.75 

0.70 

Source: JRC 

31 

 
In Figure 15 and Figure 16 are reported the planimetric RMSE 1D errors for the PNeo3 and PNeo4 sensors 
respectively. 

Figure 15. Point representation of planimetric RMSE 1D errors calculated on ortho images using JRC ICPs dataset for PNeo3 

Figure 16. Point representation of planimetric RMSE 1D errors calculated on ortho images using JRC ICPs dataset – PNeo4 

Source: JRC 

Source: JRC 

From the Table 17 to Table 20 we can summarize that RMSE values obtained with RPC based ortho images 
and rigorous ones are very similar, for equivalent number of GCPs used. We can conclude that both models can 
be equally used without impacting on results. Nevertheless, from Figure 15 and Figure 16, one can see that 
with the rigorous model, results are a bit less scattered than with RPC. 

32 

 
 
 
 
8.2 

Impact on the number of GCPs used for the image processing 

In Figure 17 to Figure 22 below are presented the RMSEs measured using the RPC model for different number 
of GCPs using ERDAS and ENVI software for 3 types of acquisition angle (5, 20 and 30 degrees off-nadir angles) 
and for sensors PNeo3 and PNeo4. 

In Figure 23 and Figure 24 are presented the results using the rigorous model for the 30 degrees off-nadir 
angle and both sensors PNeo3 and PNeo4. 

Figure 17. Behaviour of RMSEs across the various number of GCPs for ERDAS and ENVI software, source image 5˚off nadir 
– PNeo3, RPC modelling 

Figure 18. Behaviour of RMSEs across the various number of GCPs for ERDAS and ENVI software, source image 5˚off nadir 
– PNeo4, RPC modelling 

Source: JRC 

Source: JRC 

33 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 19. Behaviour of RMSEs across the various number of GCPs for ERDAS and ENVI software, source image 20˚off 
nadir – PNeo3, RPC modelling 

Figure 20. Behaviour of RMSEs across the various number of GCPs for ERDAS and ENVI software, source image 20˚off 
nadir – PNeo4, RPC modelling 

Source: JRC 

Figure 21. Behaviour of RMSEs across the various number of GCPs for ERDAS and ENVI software, source image 30˚off 
nadir – PNeo3, RPC modelling 

Source: JRC 

34 

 
 
 
 
 
 
 
Source: JRC 

Figure 22. Behaviour of RMSEs across the various number of GCPs for ERDAS and ENVI software, source image 30˚off 
nadir – PNeo4, RPC modelling 

Figure 23. Behaviour of RMSEs across the various number of GCPs for ERDAS and ENVI software, source image 30˚off 
nadir – PNeo3, Rigorous modelling 

Source: JRC 

Source: JRC 

35 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 24. Behaviour of RMSEs across the various number of GCPs for ERDAS and ENVI software, source image 30˚off 
nadir – PNeo4, Rigorous modelling 

Source: JRC 

Looking at the above Figure 17 to Figure 24 we can draw the following conclusions and recommendations: 

  As expected the number of GCPs is impacting the quality and accuracy of the orthorectification process; 
  With  Pléiades  Neo  images,  apart  for  acquisition  <5º  off-nadir,  it  is  recommended  to  always  use  a 
minimum  of  3  GCPs  when  using  RPC  modelling  to  possibly  obtain  an  orthophoto  within  requested 
accuracy; 

  Nevertheless, still using RPC modelling, a further increase of number of GCPs (i.e. higher than 3 GCPs) 

does not bring substantial improvement of RMSE values; 

  When applying the rigorous model, there is a need to use at least 6 GCPs, but then also for this model, 

a further increase of GCP number does not improve significantly the positioning accuracy. 

In  the  Table  21  below,  are  reported  the  results  obtained  with  the  orthoimagery  produced  by  the  Airbus 
automatic chain. At the time of the work, the automatic processing chain was still under development and was 
operating without using any GCPs. One can observe that results are often out of specification when acquisition 
angle is > 5º off-nadir. 

Table 21. Obtained quality control results (RMSE1D) on ortho images produced by applying Airbus automatic chain, using 
JRC ICPs dataset – PNEO 3 and PNEO 4 

PNeo 3 

PNeo 4 

ONA 

RMSEx  

RMSEy  

RMSEx  

RMSEy  

5 

20 

30 

0.71 

2.32 

1.14 

0.81 

1.24 

1.00 

Source: JRC 

0.68 

1.41 

1.61 

0.89 

1.09 

1.74 

8.3 

Impact of software used 

Looking at Figure 17 to Figure 22 we can summarize that both software (ERDAS, ENVI) produce ortho imagery 
of a very similar geometric accuracy. To note that for ENVI software the RMSEx, when acquisition angle is 30º 
off-nadir, seems to provide anomalous values. This should be further investigated, but there was not a sufficient 
number of images available, nor the time available to do. 

36 

 
 
 
 
8.4 

Impact of image acquisition angles 

In Figure 25 to  

Figure 28 below are presented, in a different way that figures 17 to 22, the RMSEs obtained with RPC and 
rigorous models with Erdas and ENVI software, for 3 types of acquisition angle (5, 20 and 30 degrees off-nadir 
angles) and using different number of GCPs and thus for the sensor Pléiades Neo 3. 

Figure 29 to Figure 32 provide the same elements by for Pléiades Neo 4 sensor. 

Figure  25. Graph of RMSEs as a function of the number of GCPs and off nadir angle, ERDAS software, RPC modelling, 
Pléiades Neo 3 

Figure 26. Graph of RMSEs as a function of the number of GCPs and off nadir angle, ENVI software, RPC modelling, Pléiades 
Neo 3  

Source: JRC 

Figure 27. Graph of RMSEs as a function of the number of GCPs and off nadir angle, ERDAS software, Rigorous modelling, 
Pléiades Neo 3 

Source: JRC 

Source: JRC 

37 

 
 
 
 
 
 
 
 
 
 
Figure 28. Graph of RMSEs as a function of the number of GCPs and off nadir angle, ENVI software, Rigorous modelling, 
Pléiades Neo 3 

Figure  29. Graph of RMSEs as a function of the number of GCPs and off nadir angle, ERDAS software, RPC modelling, 
Pléiades Neo 4 

Source: JRC 

Figure 30. Graph of RMSEs as a function of the number of GCPs and off nadir angle, ENVI software, RPC modelling, Pléiades 
Neo 4 

Source: JRC 

Figure 31. Graph of RMSEs as a function of the number of GCPs and off nadir angle, ERDAS software, Rigorous modelling, 
Pléiades Neo 4 

Source: JRC 

38 

 
 
 
 
 
 
 
 
 
Source: JRC  

Figure 32. Graph of RMSEs as a function of the number of GCPs and off nadir angle, ENVI software, Rigorous modelling, 
Pléiades Neo 4 

Source: JRC 

Comparing the results displayed in Figure 25 to Figure 32 we can draw the following conclusions: 

Unsurprisingly, the 1-D RMS errors are sensitive to the off nadir angle of acquisition, the higher the angle, 
the higher the errors induced. 

For both Neo sensors, with 30 deg. ONA, obtained Orthoimages  becomes out of requested positional 
accuracy if no GCPs are used. For PNeo 4 sensor, this is even observed with 20 Deg. ONA; 

With RPC modelling, obtained imagery are also out of requested positional accuracy when 30 deg. ONA 
even if number of GCPs are high; 

A systematic bias is observed in the Easting direction (RMSEx) which decreases with the increasing off 
nadir angle of the acquired image. Reasons for these observed biases have not been further analysed 
since corresponding RMSEs are within requested specifications. 

8.5 

Impact of Pleaides sensors 

When analysing Figure 25 to Figure 32, one can observe that: 

 

 

For Pléiades NEO 4 a bias is observed toward the north direction (RMSEy) that increases with 
increasing off-nadir nadir angles of the source image. 
Some other differences can be observed between the two sensors depending on the acquisition 
angles or the number of GCPS used for their ortho rectification. Nevertheless, these differences can 
be considered as not significant (could be confirmed with more images), thus both sensors can be 
considered as providing similar ortho products. 

8.6  Summarising values 

For Pléiades Neo 3 sensor, the obtained results with RPC and more than 3 GCPs used were: 

o  5 deg. off Nadir 
 
 
o  20 deg. off nadir 

RMSEx = 0.84 m 
RMSEy = 0.88 m 

39 

 
 
 
 
 
 
RMSEx = 0.86 m 
RMSEy = 0.70 m 

 
 
o  30deg. off nadir 
 
 

RMSEx = 1.28 m 
RMSEy = 0.67 m 

For Pléiades PNeo 4 sensor the obtained results with RPC and more than 3 GCP used were: 

RMSEx = 0.77 m 
RMSEy = 0.70 m 

RMSEx = 1.05 m 
RMSEy = 0.83 m 

o  5 deg. off nadir 
 
 
o  20 deg. off nadir 
 
 
o  30 deg. off nadir 
 
 

RMSEx = 1.20 m 
RMSEy = 0.83 m 

Both software packages Erdas and ENVI perform equally. 
These obtained values are all within the requested spatial accuracy to qualify Pléiades Neo VHR data to be used 
in the frame of the CAP 2014+ direct payments’ management. 

40 

 
 
 
 
 
9  Conclusions 

In the light of the benchmark tests performed, both by Airbus and independently by the JRC, on Pléiades NEO 
ortho products, one can draw the following conclusions: 

  On average, the Neo ortho imagery geometric accuracy meets the requirements of RMSEx ≤1.25m 

and RMSEy ≤1.25 for the VHR prime profiles defined in the VHR profile based technical specifications; 

  Consequently, Neo ortho imagery geometric accuracy meets the requirement of 5 m 1D RMSE 

 

 

 

corresponding to the VHR backup profile defined in the VHR profile based technical specifications. 
These results were obtained for tests with imagery acquired with 5˚, 20˚ and 30˚ Off Nadir Angles, 
with at least 3 GCPs use for orthorectification and with RPC or rigorous models. 
It must be noted that when rectification were made without GCPs, results obtained become out of 
specification for acquisitions 30˚ ONA and even 20˚ in some case for Pléiades Neo4 sensor. 
To note also, that at the time of the benchmark analysis, Airbus production chain was not yet fully 
finalised and internal references were not yet implemented. Airbus recently informed us that, 
according to the tests they performed using internal references that will be implemented soon in its 
production chain, the results are within the specifications whatever the PNEO sensor for angles up to 
30° ONA. This should be further confirmed but the JRC benchmarking work was already concluded at 
the time of this notification. 

One should note also that, sometimes, atypical results were obtained such as higher RMSE observed for near 
nadir  acquisition  compared  to  20˚  or  30˚  ONA.  However,  the  number  of  tested  images  was  insufficient  to 
further investigate this phenomenon. 

Finally, one should remind that the available JRC’s GCPs dataset is now dating. Several points became unreliable 
since changes occurred on the ground. Even if these points were generally discarded, one can argue that RMSEs 
estimated may hold some uncertainty. JRC is conscious of that fact, but since the sensor benchmarking activity 
was to be stopped with the entry into force of the new CAP (January 2023), the organisation of a new GCP 
dataset acquisition campaign over Maussane area was not judged imperative. 

For  the  CAP  2023+,  new  technical  specifications  will  be  set  to  comply  with  the  new  VHR  imagery  use 
requirements i.e. in the frame of Quality Assessment processes. Should sensor benchmarking tests be put in 
place, they would need to be done according to these up-dated technical specifications. 

41 

 
 
 
 
References 

i. 

ii. 

iii. 

iv. 

v. 

vi. 

vii. 

viii. 

ix. 

x. 

xi. 

xii. 

xiii. 

xiv. 

xv. 

xvi. 

Kapnias,  D.,  Milenov,  P.,  Kay,  S.  (2008)  Guidelines  for  Best  Practice  and  Quality  Checking  of  Ortho 
Imagery. Issue 3.0. Ispra  

Image  Acquisition  Specifications 

VHR 
https://g4cap.jrc.ec.europa.eu/g4cap/Portals/0/Documents/D.3.1%20VHR_Specs_2022_final_new.pdf 

(CwRS  and  LPIS  QA)  

the  CAP 

checks 

for 

Nowak Da Costa, J., Tokarczyk P., 2010. Maussane Test Site Auxiliary Data: Existing Datasets of the 
Ground Control Points. The pdf file received on 06.02.2014 via FTP.  

Lucau,  C.,  Nowak  Da  Costa  J.K.  (2009)  Maussane  GPS  field  campaign:  Methodology  and  Results. 
Available 
at 
http://publications.jrc.ec.europa.eu/repository/bitstream/111111111/14588/1/pubsy_jrc56280_fmp11
259_sci-tech_report_cl_jn_mauss-10-2009.pdf 

Lucau, C. (2012) Maussane GNSS field campaign 21-26 November 2012  

Maussane test site (& geometry benchmarks). KO-Meeting-Presentation January 30, 2014.  

Nowak Da Costa, J.K., Walczynska, A. (2011). Geometric Quality Testing of the WorldView-2 Image Data 
Acquired  over  the  JRC  Maussane  Test  Site  using  ERDAS  LPS,  PCI  Geomatics  and  Keystone  digital 
photogrammetry 
at 
Initial 
http://publications.jrc.ec.europa.eu/repository/bitstream/111111111/22790/1/jrc60424_lb-nb-
24525_en-c_print_ver.pdf 

Findings  with  ANNEX.  Available 

packages 

software 

– 

Nowak Da Costa, J.K., Walczynska, A. (2010). Geometric Quality Testing of the Kompsat-2 Image Data 
Acquired  over  the  JRC  Maussane  Test  Site  using  ERDAS  LPS  and  PCI  GEOMATICS  remote  sensing 
software. 
at 
http://publications.jrc.ec.europa.eu/repository/bitstream/111111111/15039/1/lbna24542enn.pdf 

Available 

Nowak Da Costa, J.K., Walczynska, A., 2010. Evaluating the WorlView-2, GeoEye-1, DMCII, THEOS and 
KOMPSAT-2  imagery  for  use  in  the  Common  Agricultural  Policy  Control  with  Remote  Sensing 
Programme. Scientific presentation at the 16th Conference on ‘’Geomatics in support of the CAP" in 
Bergamo, Italy, 24-26 November 2010. JRC Publication Management System.  

Grazzini, J.,  Astrand, P., (2013). External quality control of Pléiades orthoimagery. Part II: Geometric 
testing  and  validation  of  a  Pléiades-1B  orthoproduct  covering  Maussane  test  site.  Available  at 
http://publications.jrc.ec.europa.eu/repository/handle/JRC83367  

Grazzini, J., Lemajic, S., Astrand, P., (2013). External quality control of Pléiades orthoimagery. Part I: 
Geometric benchmarking and validation of Pléiades-1A orthorectified data acquired over Maussane 
test site. Available at http://publications.jrc.ec.europa.eu/repository/bitstream/111111111/29541/1/lb-
na-26-101-en-n.pdf 

Grazzini,  J.,  Astrand,  P.,  (2013).  External  quality  control  of  SPOT6.  Geometric  benchmarking  over 
Maussane 
for  positional  accuracy  assessment  orthoimagery.  Available  at 
http://publications.jrc.ec.europa.eu/repository/bitstream/111111111/29232/1/lb-na-26-103-en-n.pdf 

test 

site 

Blanka Vajsova, B., Walczynska, A., Åstrand, P., Bärisch, S., Hain, S., (2014). New sensors benchmark 
report on Kompsat-3. Geometric benchmarking over Maussanne test site for CAP  purposes. Available 
at http://publications.jrc.ec.europa.eu/repository/bitstream/JRC93093/lb-na-27064-en-n.pdf 

Blanka Vajsova, B., Walczynska, A., Åstrand, P., Bärisch, S., Hain, S., (2015). New sensors benchmark 
report on WorldView-3. Geometric benchmarking over Maussane test site for CAP purpose. Available 
at  http://publications.jrc.ec.europa.eu/repository/bitstream/JRC99433/reqno_jrc99433_lb-na-27673-
en-n.pdf 

https://www.l3harrisgeospatial.com/Software-Technology/ENVI 

https://hexagon.com/products/erdas-imagine 

42 

 
 
 
List of abbreviations and definitions  

ADS40   Airborne Digital Sensor 40 

AOI 

CAP 

Area of Interest 

Common Agricultural Policy  

CE90 

Circular Error 90% 

CwRS  Control with Remote Sensing 

DEM   Digital Elevation Model 

EQC 

External Quality Control  

EUSI  

European Space Imaging 

GCP 

Ground Control Point 

GIS 

Geographic Information System 

GNSS   Global Navigation Satellite System 

GPS  

Global Positioning System  

GSD 

Ground Sample Distance 

ICP 

IQC 

IR 

Independent Check Point 

Internal Quality Control 

Infrared 

JRC  

Joint Research Centre 

NED 

Near Infrared, Red Edge, Deep Blue 

NIR  

Near-infrared  

ONA 

Off-Nadir Angle 

PAN 

Panchromatic 

QC 

Quality Check 

RGB 

Red, Green, Blue 

RMSE   Root Mean Square Error 

RPC  

Rational Polynomial Coefficients 

STRM 

Shuttle Radar Topography Mission 

UTM  

Universal Transverse Mercator 

VHR  

Very High Resolution 

VNIR   Visible and Near-InfraRed  

WGS84  World Geodetic System '84 

PNeo 

Pléiades Neo 

1-D 

One-dimensional 

43 

 
 
 
List of figures 

Figure 1. Location of the test site and its AOI ................................................................................................................................................9 

Figure 2. Distribution of GCPs available in the AOI, the background image is an elevation map of the area. . 11 

Figure 3. Distribution of 9 GCPs selected for orthorectification process, the background image represents an 
elevation map of the area and the red square corresponding to the study AOI. ................................................................... 12 

Figure 4. Distribution of 9 GCPs selected for internal control, the background image represents an elevation 
map of the area and the red square corresponding to the study AOI........................................................................................... 13 

Figure 5. Relief of the study AOI ......................................................................................................................................................................... 14 

Figure 6. Interface OneAtlas with order parameters .............................................................................................................................. 16 

Figure 7. View in natural colour (RGB) and false colour (NED) of one acquired scene .................................................... 17 

Figure 8. Standard benchmarking procedure ................................................................................................................................................ 19 

Figure 9. RMSEx and RMSEy depending on the sensor ......................................................................................................................... 22 

Figure 10. RMSEx and RMSEy depending on the acquisition angle .............................................................................................. 23 

Figure 11. RMSEx and RMSEy depending on the orthorectification model used .................................................................. 23 

Figure 12. RMSEx and RMSEy depending on the number of GCPs used ................................................................................... 24 

Figure 13. RMSEx and RMSEy depending on the number of GCPs used ................................................................................... 24 

Figure 14. ICPs dataset used by JRC in the EQC of PNeo ortho imagery. ................................................................................ 27 

Figure 15. Point representation of planimetric RMSE 1D errors calculated on ortho images using JRC ICPs 
dataset for PNeo3 .......................................................................................................................................................................................................... 32 

Figure 16. Point representation of planimetric RMSE 1D errors calculated on ortho images using JRC ICPs 
dataset – PNeo4 .............................................................................................................................................................................................................. 32 

Figure 17. Behaviour of RMSEs across the various number of GCPs for ERDAS and ENVI software, source 
image 5˚off nadir – PNeo3, RPC modelling ................................................................................................................................................... 33 

Figure 18. Behaviour of RMSEs across the various number of GCPs for ERDAS and ENVI software, source 
image 5˚off nadir – PNeo4, RPC modelling ................................................................................................................................................... 33 

Figure 19. Behaviour of RMSEs across the various number of GCPs for ERDAS and ENVI software, source 
image 20˚off nadir – PNeo3, RPC modelling ................................................................................................................................................ 34 

Figure 20. Behaviour of RMSEs across the various number of GCPs for ERDAS and ENVI software, source 
image 20˚off nadir – PNeo4, RPC modelling ................................................................................................................................................ 34 

Figure 21. Behaviour of RMSEs across the various number of GCPs for ERDAS and ENVI software, source 
image 30˚off nadir – PNeo3, RPC modelling ................................................................................................................................................ 34 

Figure 22. Behaviour of RMSEs across the various number of GCPs for ERDAS and ENVI software, source 
image 30˚off nadir – PNeo4, RPC modelling ................................................................................................................................................ 35 

Figure 23. Behaviour of RMSEs across the various number of GCPs for ERDAS and ENVI software, source 
image 30˚off nadir – PNeo3, Rigorous modelling ..................................................................................................................................... 35 

Figure 24. Behaviour of RMSEs across the various number of GCPs for ERDAS and ENVI software, source 
image 30˚off nadir – PNeo4, Rigorous modelling ..................................................................................................................................... 36 

Figure 25. Graph of RMSEs as a function of the number of GCPs and off nadir angle, ERDAS software, RPC 
modelling, Pléiades Neo 3 ......................................................................................................................................................................................... 37 

Figure 26. Graph of RMSEs as a function of the number of GCPs and off nadir angle, ENVI software, RPC 
modelling, Pléiades Neo 3 ......................................................................................................................................................................................... 37 

Figure 27. Graph of RMSEs as a function of the number of GCPs and off nadir angle, ERDAS software, 
Rigorous modelling, Pléiades Neo 3 .................................................................................................................................................................... 37 

44 

 
Figure 28. Graph of RMSEs as a function of the number of GCPs and off nadir angle, ENVI software, Rigorous 
modelling, Pléiades Neo 3 ......................................................................................................................................................................................... 38 

Figure 29. Graph of RMSEs as a function of the number of GCPs and off nadir angle, ERDAS software, RPC 
modelling, Pléiades Neo 4 ......................................................................................................................................................................................... 38 

Figure 30. Graph of RMSEs as a function of the number of GCPs and off nadir angle, ENVI software, RPC 
modelling, Pléiades Neo 4 ......................................................................................................................................................................................... 38 

Figure 31. Graph of RMSEs as a function of the number of GCPs and off nadir angle, ERDAS software, 
Rigorous modelling, Pléiades Neo 4 .................................................................................................................................................................... 38 

Figure 32. Graph of RMSEs as a function of the number of GCPs and off nadir angle, ENVI software, Rigorous 
modelling, Pléiades Neo 4 ......................................................................................................................................................................................... 39 

45 

 
 
List of tables 

Table 1. General characteristics of the Pléiades Neo constellation ..................................................................................................4 

Table 2. Orbital characteristics and viewing capability .............................................................................................................................4 

Table 3. Key attributes of the Pléiades Neo Constellation .....................................................................................................................5 

Table 4. Standard product options delivered by Image Production Facility .................................................................................6 

Table 5. Ground Control Points available for the Maussane test site ......................................................................................... 10 

Table 6. Collection used for the 9 GCPs selected for the orthorectification process ......................................................... 11 

Table 7. Collection used for the 9 GCPs selected for the internal control ................................................................................ 12 

Table 8. ORI and CORI accuracy specifications .......................................................................................................................................... 15 

Table 9. DTM vertical accuracy specifications ............................................................................................................................................ 15 

Table 10. Aerial Orthomosaics Specifications ............................................................................................................................................ 15 

Table 11. Real angles of the primary product across study AOI .................................................................................................... 16 

Table 12. Tested Scenarios ..................................................................................................................................................................................... 19 

Table 13. Distribution of tests according to parameters ..................................................................................................................... 21 

Table 14. Acquired Images ...................................................................................................................................................................................... 22 

Table 15. Identical check points specifications .......................................................................................................................................... 26 

Table 16. ICPs overview ............................................................................................................................................................................................ 27 

Table 17. Obtained quality control results (RMSE1D) on ortho image produced by applying Rational Function 
Modelling, using JRC ICPs dataset – PNeo3 ................................................................................................................................................... 30 

Table 18. Obtained quality control results (RMSE1D) on ortho image produced by applying Rational Function 
Modelling, using JRC ICPs dataset – PNeo4 ................................................................................................................................................... 30 

Table 19. Obtained quality control results (RMSE1D) on ortho image produced by applying Rigorous 
Modelling, using JRC ICPs dataset – PNeo3 ................................................................................................................................................... 31 

Table 20. Obtained quality control results (RMSE1D) on ortho image produced by applying Rigorous 
Modelling, using JRC ICPs dataset – PNeo4 ................................................................................................................................................... 31 

Table 21. Obtained quality control results (RMSE1D) on ortho images produced by applying Airbus automatic 
chain, using JRC ICPs dataset – PNEO 3 and PNEO 4 .............................................................................................................................. 36 

46 

 
 
GETTING IN TOUCH WITH THE EU 

In person 

All over the European Union there are hundreds of Europe Direct centres. You can find the address of the centre nearest you online 
(european-union.europa.eu/contact-eu/meet-us_en). 

On the phone or in writing 

Europe Direct is a service that answers your questions about the European Union. You can contact this service: 

— by freephone: 00 800 6 7 8 9 10 11 (certain operators may charge for these calls), 

— at the following standard number: +32 22999696, 

— via the following form: european-union.europa.eu/contact-eu/write-us_en. 

FINDING INFORMATION ABOUT THE EU 

Online 

Information  about  the  European  Union  in  all  the  official  languages  of  the  EU  is  available  on  the  Europa  website  (european-
union.europa.eu). 

EU publications 

You  can  view  or  order  EU  publications  at  op.europa.eu/en/publications.  Multiple  copies  of  free  publications  can  be  obtained  by 
contacting Europe Direct or your local documentation centre (european-union.europa.eu/contact-eu/meet-us_en). 

EU law and related documents 

For access to legal information from the EU, including all EU law since 1951 in all the official language versions, go to EUR-Lex (eur-
lex.europa.eu). 

Open data from the EU 

The portal data.europa.eu provides access to open datasets from the EU institutions, bodies and agencies. These can be downloaded 
and reused for free, for both commercial and non-commercial purposes. The portal also provides access to a wealth of datasets 
from European countries.