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RESEARCH ON SAR IMAGE MATCHING TECHNOLOGY BASED ON SIFT 
LIU Jing-zheng, YU Xu-chu 
Zhengzhou Institute of Surveying and Mapping, 66 Longhai Road, Zhengzhou 
450052,China-ljzchxy@163.com 
Commission I, WG 1/2 
KEYWORDS: SAR, Curvelet, De-noising, SIFT, Image Matching 
ABSTRACT: 
Image matching is one of the key technologies in remote sensing image fusion and navigation. Image matching of Synthetic 
Aperture Radar (SAR) is a process to find relationship of pixels in many SAR images, which directly involves and affects the 
application of SAR image in many areas such as mapping, integrated navigation and image fusion. In order to improve the searching 
speed in matching, pyramid strategy is used. Considering the characteristic of low S/N ratio in SAR image, curvelet is introduced in 
preprocessing SAR images. l,For the great metamorphoses between SAR images, direct Scale Invariant Feature Transform (SIFT) is 
used in matching of destination image and referenced image which are similar with each other in greyscale. 2, edge extraction is 
implemented in SAR images acquired at different times and on different orbits by using Canny operator, and then SIFT key points is 
extracted to match the images. Combined with correlation coefficient controlling method, error matching points are wiped off and 
good result is acquired. 
1. INTRUDUCTION 
Image matching is to make use of image data acquired by 
sensor and compare it with referenced image to obtain 
corresponding object position in referenced image. Image 
matching is very important in computer vision, image fusion, 
and object recognition and tracing. 
There are severe speckle noises in SAR image and these noises 
have more disturbances on image processing and recognition 
than regular noises do and become a big obstacle in applications 
of SAR image, such as auto recognition and matching. In this 
paper, Curvelet method was introduced to denoise the SAR 
image. In 2D image processing that uses wavelet transformation, 
separated transformation cores are used to implement wavelet 
transformation horizontally and vertically independently. The 
local module maximum of parameter of transformation can only 
show that the position where wavelet coefficient appears is 
across edge, but can not express the along edge information, 
which makes traditional wavelet transformation restricted in 2D 
image processing. To solve this problem, Donoho put forward 
Curvelet transformation, of which anisotropism is very suitable 
to express the edge effectively. 
Because of difference of emission source location, azimuth and 
height in SAR, there are rotations, zooming and metamorphoses 
in different SAR images. Even SAR images that obtained in one 
source location in different time have great difference in 
grayscale. These differences, which differ from traditional 
optical image, make it difficult to find correspondence points in 
SAR image of same source. Correlation coefficient matching is 
not able to match to ideal position, either, and as the increase of 
window size, the computing speed decreases obviously, which 
makes it hard to apply to real-time matching. In this paper, 
scale-invariant feature transformation was introduced to 
matching of SAR images of same source. 
different views and image distortion. David G.Lowe (2004) 
summarized the existing feature detecting methods based on 
invariant technology and put forward an operator which 
describes the local feature of image — SIFT operator. This 
operator maintains invariant in scale space, image zooming, 
rotation and even affine transformation. Afterward, Y.Ke 
improved this operator by replacing histogram with PCA in 
describing sub-parts. 
2. FILTER PROCESSING OF SAR IMAGE 
2.1 The Characteristic of SAR image 
Comparing with traditional optical image, SAR image, which is 
obtained by processing echo signal, has its own features and 
cannot be applied to matching directly. The projection style of 
radar image is different from that of optical image and the 
image points are recorded according to the distance between 
object and center of antenna. So, the geometry proposition of 
SAR does not fit for the vision habit of human. Comparing with 
ortho-image, SAR image has larger distortion and stretching 
than optical image. SAR emits coherent waves and when these 
waves touch the object, the overlap of random scattering signals 
of each scatter unit on the scatter plane will cause coherent 
speckles. Speckle noise presents the sharp change of grayscale 
and its effect is larger than other noise, so it has serious impact 
on feature extraction, image matching and object recognition on 
SAR image. 
In order to solve these problems, SAR image should be pre- 
processed before matching. Pre-procession of image includes 
contrast enhancement and denoising of SAR image. Contrast 
enhancement of image can adopt partition linear transform to 
guarantee that image is not distorted. In this paper, curvelet is 
introduced to denoise the SAR image. 
The early work of Lowe (1999) extended the feature to void the 
effect of scale, which also overcome the effect caused by