COREGISTRATION BASED ON SIFT ALGORITHM FOR SYNTHETIC APERTURE 
RADAR INTERFEROMETRY 
Fangting Li 3, *, Guo Zhang 3 , Jun Yan 3 
3 State key Library of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 
Wuhan, 430079, China, lifangtingl985@163.com, guozhang@whu.edu.cn, yanjun_pla@263.net 
KEY WORDS: Coregistration , INSAR, SIFT, 
ABSTRACT: 
Single-look complex image coregistration is the key step of Synthetic Aperture Radar(SAR) Interferometry. The precision of the 
coregistration results have a direct effect on the quality of the SAR interferogram generated, thereby it will influence the accuracy of 
extracting DEM. In this paper, SIFT (Scale Invariant Feature Transform) algorithm will be used in Single-look complex image 
coregistration, and in the pilot study, experiments have provn the method is useful. 
1. INTRODUCTION 
1.1 Single-look complex image coregistration 
Single-look complex (SLC) image coregistration is the key step 
of synthetic aperture radar interferometry. The precision of the 
coregistration results have a direct effect on the quality of the 
SAR interferogram generation, thereby it will influence the 
accuracy of extracting DEM. When accurately coregistrater the 
two SAR complex images from the two approaching tracks, 
their interferometric phase differencing images will display 
stripes. The changes of the stripes include the terrain undulation 
information. If the two images were not precisely 
coregistratered, the interference stripes generated will be 
blurred, or even no interference stripes will be generated. At 
present, in order to achieve sub-pixel level accuracy, the 
generally used coregistration method for complex image is 
multi-stage coregistration, the commonly used method is: the 
rough image coregistration based on the orbit information or on 
the intervention of users, which is the first stage of 
coregistration; image coregistration based on pixel level, which 
is the second stage of coregistration; and image coregistration 
based on sub-pixel level, which is the third stage of 
coregistration. 
1.2 Original solutions 
SLC image coregistration is to calculate the coordinate 
projection relationship between a master image and slave image, 
and then to take use of this relationship to implement 
coordinates transformation, image interpolation and resampling. 
The interference measurement requires the accuracy of image 
matching to be on a sub-pixel level. Therefore, the SAR image 
matching includes two steps - coarse and precise matching. 
The coarse matching can be effectuated by using satellite 
orbital parameters or manually selecting a few feature points to 
calculate the deviation values, Ar and Ac, in direction (row of 
image) and in distance (column of image) between the master 
image and slave image. The deviation values are relatively 
rough values, whose accuracy is usually on pixel level. The 
purpose of coarse matching is to provide an initial value for the 
stereo-pair pixel searching of precise matching. 
The precise matching method of SLC images, firstly, samples 
the master image and slave image, then identifies N uniform 
distributed control feature points on the master image, and 
selects the matching window of a certain size, whose center is 
the control feature points. According to the deviation values of 
coarse matching, the precise matching method then selects a 
larger search window than the matching window in the 
corresponding position of the slave image. Based on a certain 
order, it moves the matching window pixel by pixel to calculate 
the indicator values for the two windows. The point with the 
best matching indicator value in the search window will be 
chosen as the stereo-pair point in the slave image. Through 
these processes, the coordinates in two images of the stereo-pair 
points are obtained. 
According to the coarse and precise matching mentioned above, 
we can get N coordinate pairs of N feature stereo-pair points. 
The polynomial (such as third-order) model is used to simulate 
the coordinate projection relationship of the master image and 
slave image. The parameters of the polynomial model can be 
solved by measurements of N coordinate pairs and the least- 
squares algorithm. The coordinate transformation relationship 
of the pair-image is achieved. Finally, coordinates 
transformation and resampling can be carried out based on this 
relationship. In this way, the slave image is transformed into the 
space of master image, which is ready to produce the 
interferometry image. 
The three matching method analyzed in this study are all 
precise matching methods. The general algorithms and the 
processes are all almost the same with these three methods. The 
only differences are the method for calculating the indicator 
value in the matching window and the standard of selecting the 
stereo-pair point. The details of these three methods will be 
discussed below. 
(1) The coherent coefficient method uses the coherent 
coefficient as the indicator value. The coherent coefficient of 
the stereo-pair pixel of matching window and the target window 
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