AUTOMATED REGISTRATION WITH HIGH TO MEDIUM RESOLUTIONSATELLITE DATA
Kyaw Sann OO*, Masataka TAKAGI*
Kochi University of Technology*

ABSTRACT: This is an ongoing engineering need for precise geodetic measurements. Recent measurements
could not precisely predict the failure events of civic structures and ground position of specific locations.
Precise measurement could be an interesting field of study for geodetic surveyors and researchers. There aremany applications which require highly accurate (± 0.25 meters) measurements for geodetic surveying eventhough precision measurement techniques are still at the research stage. By applying the method of leastsquares it is possible to match two images precisely using a predefined template function. Consequently,
image to image geometric correction could be conducted to fetch registered output image products. The
registration could be done from high resolution satellite data (IKONOS) to medium resolution satellite data(ASTER) or from high resolution to high resolution satellite data (ALOS). In the present research, rational
polynomial coefficient (RPC) orthorectification with digital Surface model (DSM) and least squares image
matching technique were assembled. The developed numerical algorithm will be resembled to automatic androbotic. Finally, an advanced and precise application system was developed. This process had very good
output with the total residual error of 0.06 pixel in high to medium resolution satellite data registration. Thisadvanced application will be applicable in the field of engineering and social management. In addition,
recent research trends on this topic are examined and evaluated. Future directions for research are suggested
in the last section of this paper.
KEYWORDS: GCPs, registration
1. INTRODUCTION

land development. Despite the apparent usefulnessof remotely sensed data for social purposes, however,

There is increased interest today in making scientificremotely sensed images have not been a popular for

progress through the use of remotely sensed data insocial science research, for several reasons (Ronald

social science research (Ronald R. et al., 1998).

R. et al., 1998).

Remote sensing is a not new technology. Aerial
photographs have been in widespread use for aFirst, the variables of greatest interest to many

half-century (Carls, 1947) and satellite images for asocial scientists are not readily measured from the air.

quarter-century (e.g., Estes et al., 1980; Morain, in

Many social scientists find visible human artifacts

this volume). These images have been put to various

such as buildings, crop fields, and roads lesssocially useful purposes, including making landslideinteresting than the abstract variables that explain

monitoring, infrastructure liability failure monitoring, their appearance and transformations. Changing land
crop forecasts, predicting serve storms, and planninguse, road and building construction, and the like areregarded as manifestations of more importantavailability of ancillary information.

variables, such as government policies, land-tenure
rules, distributions of wealth and power, market

Image registration is the art of locating digitalmechanisms, and social customs, none of which is

pixel of the image with relative position of thedirectly reflected in the bands of the electromagneticground. The geometry of an orthorectified highspectrum. Thus social scientists are likely to beresolution satellite image (IKONOS) is fixed byskeptical that remote sensing can measure anythingRPC data. These RPC coefficients, along withconsidered important in their fields of study (Turner,measurements of the plane's three dimensionalin press).

position relative to the ground, can be used toremove spatial distortions caused by topography,

A related issue is that social science is generallycalledortho-rectification.

Byremoving

themore concerned with why things happen than wheregeometric distortions and the topographic distortions

they happen (Turner, in press). Even areas of social

satellite data can be used for very precise registration

science in which one might expect a spatial

to other uncorrected satellite data.

orientation are curiously a spatial. For example,
while is seems almost self-evident that spatial

Recently, there are several approach to register

propinquity must be a factor in the shaping of social

images to use in real world applications. The mostnetworks, it is only recently that the spatial aspects

attracting method was a method that combines theof social networks have been receiving attention

merits of area-based and edged-based matching was(Faust et al., 1997). Relatively few social scientists

applied to photogrammetry by Foerstner (1982),

outside the field of geography value the spatial

Ackermann (1984), and Pertl (1984). The method

explicitness that remotely sensed data provide, nor

was found to be of great potential for a variety of

do the typical social science data sets contain theimage and template matching problems. It is ofgeographic coordinates that would facilitate linking

particular value for some essential photogrammetricsocial science data and remotely sensed data.

tasks such as automated detection and measurementof fiducial marks, reseau crosses, stereomodelRemotely sensed data in this case satellite(Gruber) points, control points, the transfer andimages are spatially acquisition of ground surface

measurement of tie points, and generation of digital

dataterrain models (Gruen A., 1985). This interest will bewithmeasurement

spatial/temporal/spectralofresolution.

specificThey

arefuture work in this research.

directionally and geometrically correct with regard
to a planar frame of reference. Uncorrected satellite

2. REGISTRATION

data have spatial distortion due to terrain elevation

Registration is a process to register an input imageasto(ASTER or ALOS) to geographic coordinates andinconsistencies in the attitude of the satellite position

correct them to match with reference image(roll, pitch and yaw).

These limitations to accuracy(IKONOS) geometry. The miss registration of pixels,

are sometimes quantifiable in terms of scale,(Figure 1) could be output erroneous result toresolution and angular distortion but for the mostconjugated images processing.

part the "accuracy" of a satellite image registration isProblem: Locating error

wellasgeometricdistortions

duea function of the experience of the interpreter and•Sample point location on based image:16652.85E, 59893.97N•

Sample point location on input image:
16646.05E, 59890.73N

Figure 2: Full images with GCP Points.
Figure 1: Locating error conjugated image pair.
In this study, the IKONOS image was used as
In this experiment, the original image is roughlyreference image and assumed it was orthorectified

registered ASTER image with the easting error 6.8

image with precise location. The ASTER image wasmeters and northing error 3.24 in the specific place.

as an input image to correct. It could be raw images

It was still needed to get precise registration toor pre roughly registered image.

include in the social research analysis.
Ground control points (GCPs) is a type of tie
Selecting ground control points (GCPs), warping

points. It can be acquired from the GPS or vector

input image with reference one and resampling todata or registered reference image. In this study,

new output image are serial processes of image

GCPs points are selected from orthorectified highregistration.

resolution satellite image (IKONOS). Selection ofthe GCPs is identification of the GCPs in the2.1

SelectingGCPs,

Least

Squares

displayed images by locating pixels. Selecting

estimation/RMS error calculation

subpixel positions are supplied to provide higher

Image matching is the prior method to register

accuracy in selecting GCPs. The following facts areimages by selecting match point pairs or tie points

used to produce accurate results, when selecting(GCPs) between reference and input images. The

corresponding control points:

reference image sometime can be called based imageand the warp image is raw or input image.

•

Select numerous control points. A warpingtransformation based on many control points

The total root mean squares (RMS) error can becalculated using (Equation 1) and (Equation 2) byinputting selected GCPs points.
The RMS for a collection of N values x1, x2, ...,
xn is:

produces a more accurate result than one
based on only a few control points.

•

Select a higher density of control points inirregular or highly varying areas of theimage.

•Select points in which there are moreconfident.

Includingpointswithpoor

accuracy may generate worse results then awarp model with fewer points.
In this study, total selected ground control pointis 5 pairs and it is match (degree+1)2 polynomial
degree 1's requirement. It is a possible approach insituations with more than three pairs of fitted points.
The total residual error is 0.06 for this GCPs
selection. Based on selected GCPs, the degree ofpolynomial (in this case degree is 1 because GCPs=
5 we have according to (degree+1)2) and polynomial
coefficient can be calculated. Using least squares
estimation, determines the coefficients Kxi,j and Kyi,jof the polynomial functions by(Equation 3) and(Equation 4).

where Kx and Ky used as inputs P and Q to thenext process. This coordinate transformation wasthen used to map from Xo, Yo coordinates into Xi, Yi
coordinates. Xi, Yi are vectors of X and Y
coordinates to be fit as a function of Xo, Yo. Xo, Yo
are vectors of X and Y dependent coordinates. Thesemust have the same number of elements as Xi, Yi. Kxis a named variable that will contain the array ofcoefficients for Xi as a function of (Xo, Yo). This
Figure 3: Selected GCP Points.

parameter is returned as a (Degree+1) by (Degree+1)
element array. Ky is a named variable that will•

Select control points near the edges of thecontain the array of coefficients for Yi.

image in addition to control points near thecenter of the image.

The calculated values of those coefficientsmatrix from this sample are presenting below:

PandQ

arearrays

containing

thepolynomial coefficients. Each array must containkx00=2327.0883466

ky00=3586.4158528(N+1)2 elements (where N is the degree of thekx01=0.0001432187

ky01=0.0666028563

polynomial). According to this study, for a linear

kx10=0.0668108705

ky10=0.0000844610

transformation, P and Q contain four elements

kx11=-0.0000000113

ky11=0.0000000051

and can be a 2 x 2 array or a 4-element vector.
Pi,j contains the coefficient used to determine x',

kx, ky contains the array of coefficients for Xi, Yi

and is the weight of the term xjyi. This P and Q

as a function of (Xo, Yo). This parameter is returned

polynomial coefficients matrices are written inas a (Degree+1) by (Degree+1) element array.

the following format:
P[0,0]

Q[0,0]

transformation

P[0,1]

Q[0,1]

Warping could be performed using polynomial

P[1,0]

Q[1,0]

functions, DelaunayP[1,1]

Q[1,1]

2.2

Polynomial

warping/

Geometric

triangulation, or rotation,scaling, and translation (RST). The geometricaltransformation formula:where g[x, y] represents the pixel in the output
image at coordinate (x, y), and f [x', y'] is the pixel at(x', y') in the input image that is used to derive g[x,y]. The functions a (x, y) and b (x, y) are

where x' and y' are the locations in the basepolynomials in x and y of degree N, whose

image, x and y are the locations in the input image

coefficients are given by P and Q, and specify the(in this case ASTER), N is the polynomial degree

spatial transformation.

(degree=1), and P and Q are the polynomialcoefficients. This coordinate transformation then

2.3 Spatial transformation / image resamplingused to map from Xo, Yo coordinates into Xi, Yi

There are several methods for spatial resampling

coordinates.

such as include nearest neighbor, bilinear, and cubic
convolution. Nearest neighbor method was applied

3. RESULTin this study restraint to selected GCPs.

The accuracy checked was done by overlaid twoimages. Based on this assessment, the output image•

Nearest Neighbor

Nearest neighbor interpolation method washas satisfied result and it could be used for futuresocial management analysis works.

selected to perform this process. This resamplinguses the nearest pixel without any interpolation
to create the warped image. Using following
spatial transformation function, the nearest
neighbor interpolation method could be applied.

The following (Figure 4) is represented that theaccuracy of cross checking by images overlay.

Figure 5: Output Precision.
After registration was conducted, the resultshows that the two sample images have easting
uncertainty 0.9 and northing uncertainty 0.01 meter.
Figure 4: Result Overlay.
Each residual errors and total residual error arelisted in the following table.
Table 1: Resedual error.
Figure 6: Input and Output Comparison.
•A landmark on runway of Kochi airport

The above image comparison (Figure 6)
For the best results, GCPs should be widelyrepresented that the acceptable accuracy of image

scatter in the images and attempt to minimize theregistration. It could be possible to improve accuracy

RMS error by refining the positions of the pixels

by iterating registration. But it is impossible towith largest errors or by removing them. Errors canremove uncertainty error.

be reduced by using more points.
4. FUTURE WORKThe selected tie points are calculated by using

The method is tagged as "automated" because it

least squares estimation and the residual total error iscould be possible to execute itself when the input

0.06 pixels.

data were available in specific folder and theprogram will automatically execute using predefine

The miss locating error problem mention in theparameters for respected area.

beginning of this paper was corrected by image
registration method. The output corrected location
can be found in following (Figure 5).
••

Automated registration can be applied to avariety of location miss pointing problems. It shows

Sample point location on based image:numbers of attractive features to engineers and social16652.85 E, 59893.97 N

scientists, such as locating specific pixels to detect

Sample point location on output image:the temporal based changed of socio related features,16653.75 E, 59893.98 N

monitoring ground movement to forecast landslide

William, H. P. et al., Numerical Recipes in C, second

disasters, and so on.

edition. Cambridge University Press, USA, 1992.
It is proved that image to image registration is
successful approach to issue high accurate location
for professional used. It also could be possible toextend

theregistration

betweenorthorectified

satellite image and existing GIS data. Then it iscould be cross check by collected GPS data. It could
be benefited to GIS data updating using available
high resolution satellite data after registration.
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