Analysis Method Using Optical Satellite Data
Masahiko Nagai
Director, Center for Research and Application for Satellite Remote Sensing
Professor, Graduate School of Sciences and Technology for Innovation
Yamaguchi UniversityThermal Remote Sensing

Optical Remote Sensing

Strength of reflection

Microwave Remote Sensing

Strength of Radiation

Vegetation

Ground surface

soil

Wavelength(μｍ)
0.4
Ultravioletray© Masahiko Nagai

water

Water surface

0.7

0.9

Visible ray Near Infrared

62
Intermediate Infrared

Infrared

12
Thermal Infrared Microwave
2

Optical Remote Sensing

Thermal Remote Sensing

Microwave Remote Sensing

ALOS(True Color Image)

Pleiades(False Color Image)

ASTER(Thermal Image)

ALOS-2(SAR Image(Amplitude))

ALOS-2(SAR Image(Phase))＜Applications＞
Landslide・Volcano
Flood・Tsunami
Building Damage＜ Applications ＞
Landslide
Volcano・Lava flow
Flood・Tsunami＜ Applications ＞
VolcanoForest Fire
City Fire＜ Applications ＞
Flood・Tsunami
Landslide＜ Applications ＞
Land Deformation
Building Collapse
Liquefaction

GRUS-1, PlanetScope
WorldView, Pleiades,
SPOT, Sentinel-2
ALOS-3

GRUS-1, PlanetScope
WorldView, Pleiades,
SPOT, Sentinel-2
ALOS-3

ASTER, MODIS,

ALOS-2, Sentinel-1
TerraSAR-X,
Rardarsat

ALOS-2, Sentinel-1
TerraSAR-X,Rardarsat© Masahiko Nagai

3Introduction• Building a larger time-series training dataset for different satellites
brings following constraintso The interpretation of EO images needs expert knowledge, so annotation is atime- consuming and resource-intensive procedure.
o buying enough scenes just for training data preparation for each satellite will
be very expensive and not very practical.
o Many new micro-satellites are getting launched and these do not haveenough images in their archived data to prepare a large training dataset. Or
we have to wait till the time they get enough images captured to use ourmodels.

Satellite data harmonization• Different satellite have different wavelength definitions for bands,
along with the atmospheric influence, calibration errors, and evenorbital overpass time influences the final results.
• Harmonization tries to minimize the differences among inter- andintra- satellites data.

NDVI product Before Harmonization

NDVI product After Harmonization

How satellite harmonization performed

Overview of the calibration and harmonization* setup atYamaguchi University* Ichikawa, D.; Nagai, M.; Tamkuan, N.; Katiyar, V.; Eguchi, T.; Nagai, Y. Development and Utilization of a Mirror Array Target for
the Calibration and Harmonization of Micro-Satellite Imagery. Remote Sens. 2022, 14, 5717. https://doi.org/10.3390/rs14225717

ミラーアレイによる校正

Characteristics of Mirror Array Target
In according to the tasking and scheduling for satellite
observation, the mirror reflectors have been set up by adjustinga precise azimuth and tilt angles to get maximum reflectancefrom the mirrors.

A key for tilt angle adjustment

A key for azimuth angle adjustment

Mirror reflectors after adjustment

Observation of Mirror Array Target by GRUS-1A

Mirror array
Mirror arrayBand 1 image

Axelspace2021-02-22

Pixel value

Observation of Mirror Array Target by Cartosat2E

The spread of light spectrum around the satellite
image pixel of the ground mirror reflector
The mirror reflector can precisely estimate a sub-pixel band registration accuracy and improveimage quality of color composite images.
The distribution and spread of light energy reflected from the mirrors show that YUCARS mirror arraystation has a potentiality to construct a point spread function of in-flight image

GRUS image of YUCARS mirrors
14

Estimation of IPSF Parameter

Development of Point Spread Function – IPSF

15

Calibration by IPSF

Satellite data collection and preprocessing

Analysis of light spectrum spreadaround the pixel of ground mirror
reflector

Determine of distribution type of lightspectrum

Estimate parameters (Kernel, sigma,angle, …) and construct IPSF

Satellite image transform to frequency
domainReconstruct satellite image

Mirror reflectors and Point Spread Function for optical satellite data calibration

Result of Calibration by Mirror Array Target
GRUS1-A 2021-02-22

Original image

Improving band registration

Improved image

Deblurring the image

Transfer learning
Transferring knowledge from the networks trained on larger dataset (sourcedataset) to the target dataset containing similar but not same input data.

What if we don’t have very large dataset for satellite images with enough diversity? And how to make one?

Transfer Learning with Data Harmonization

Calibrated
GRUS-1 Image

MachineLearning

Training Data setfrom Calibrated
GRUS-1Model A

Object detection by GRUS-1

Transfer
Learning

Calibrated
PlanetScope Image

Model A

Calibrated
Sentinel-2 Image

Transfer
Learning

Object detection by PlanetScope

Model A

Object detection by Sentinel-2

Data used

Satellite constellation~200
PlanetScopesatellites

5 GRUSsatellites
GRUS1A

Spectral bands

Groundresolution

PlanetScope PS2

Panchromatic

450-900 nm

Blue

450-505 nm

Green515-585 nm

Red

620-685 nm

Red Edge705-745 nm

Near Infrared

770-900 nm

Swath57+ Km

Panchromatic

2.5 m

Multispectral

5.0 m

PlanetScope PSB.SD

Instrument

PS2

PSB.SDSpectral Bands

Blue: 455 - 515 nm
Green: 500 - 590 nm
Red: 590 - 670 nm
NIR: 780 - 860 nm

Blue:465 - 515 nm
Green:513 - 549 nm
Red:650 - 680 nm
Red-Edge: 697 - 713 nm
NIR:845 - 885 nm

Resolution

3.125 mStudy area and classes• Area:o Ube area in Yamaguchi
Prefecture, Japan.

• Classes:o Agricultureo Watero BareLando BuildUp
o Forest
Image chips were created using sliding window nonoverlappingsampling method.

Study area

Network Used• UNet** Katiyar, V.; Tamkuan, N.; Nagai, M. Near-Real-Time Flood Mapping Using Off-the-Shelf Models with SAR
Imagery and Deep Learning. Remote Sens. 2021, 13, 2334. https://doi.org/10.3390/rs13122334

Experiments by different datasets• Network trained on Original images• Trained on GRUS and transfer to PS2.
• Trained on GRUS and transfer to PSB.SD.

• Network trained on Calibrated images• Trained on GRUS and transfer to PS2.
• Trained on GRUS and transfer to PSB.SD.

Example tiles used for thetraining• The ‘Other’ class is where theclass type was not certain orcloud or cloud-shadow waspresent.
• In our study we have workedwith only five defined LULC
classes (Agriculture, Water,Bareland, Build-Up, andForest).

Part of GRUS-1 satellite image

Corresponding non-overlapping tiles

LULC classes for the previous
image-part

Corresponding non-overlapping tiles

Results
Accuracy

Agriculture Water Bare Land Build Up Forest
Original

Calibrated

GRUS-> PS2

0.71

0.79

0.63

0.72

0.82

GRUS -> PSBSD

0.73

0.83

0.65

0.71

0.82

GRUS-> PS2

0.75

0.84

0.70

0.71

0.89

GRUS -> PSBSD

0.79

0.88

0.69

0.73

0.88

Conclusion• Even when the targeted dataset is very small transfer learning withharmonization give notable improvement.
• This is an important observation as creation of large dataset for eachsatellite separately can be avoided.
• Also, Image harmonization can help us to create a larger dataset bycombining various micro-satellite images after harmonisation. Thiskind of training dataset may play an important role for future
development in the remote sensing domain. Also, this will help us tobuild a high frequency time-series dataset.

Thank you foryour kind attention