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A NOVEL EDGE-DETECTION 
BASED SEGMENTATION ALGORITHM FOR POLARIMETRIC SAR IMAGES 
Jie Yang 3, *, Ran Yang 3 , Shigao Li b , S. Shoujing Yin 3 , Qianqing Qin 3 
a State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing. Wuhan University, 
No. 129 Luo Yu Road Wuhan City 430079 China (yangjie@lmars.whu.edu.cn, yangran322@163.com, 
shoujingy@163.com, qqqin@lmars.whu.edu.cn. ) 
b National Engineering Research Center For Multimedia Software, Wuhan University, No. 129 Luo Yu Road Wuhan 
City 430079 China (sg51@163.com) 
Commission VII, WG VII/2 
KEY WORDS: Pattern Recognition, Segmentation, Image understanding, Feature extraction, Land Cover 
ABSTRACT: 
Aiming at overcoming the disadvantages of the algorithm proposed by White, R. G. (RGW) and making it applicable for PolSAR 
data classification, a novel edge-detection algorithm based on segmentation for Polarimetric SAR images is proposed in this paper. 
As one of the famous algorithms based on objected-oriented segmentation, RGW has been used extensively in image segmentation. 
However, it also has some disadvantages such as a large number of small regions in the segmentation result. In this paper, after a 
series of pre-processing including edge enhancement and edge detection, the initial segmented small regions are merged according 
to the region distance defined by distance of coherence matrix of PolSAR data, and discrete points (at the position of edges) are 
merged into regions according to Wishart distance. We applied the new algorithm to NASA/JPL AIRSAR L band data of Flevoland, 
Netherlands. Compared with RGW, experiment results demonstrate that the region of Flevoland resulting from our method get 
superior segment results. In addition, a better classification result is derived from the segmentation result. What’s more, the method 
has the advantage of edge holding while implementing classification. 
1. INTRODUCTION 
1.1 General Instructions 
Most recent classification works have concentrated on 
point-based classification of SAR data. These methods are 
mainly based on spectral characteristic of pixels. But there may 
be some problems with those methods when applied to high 
resolution images, e.g. there are discrete points existing in the 
results and edges between regions are not clear. Also these 
methods are only lower level understanding of images, since 
consideration of single pixel characteristic in isolation neglects 
the rich and complex information of land covers. In such cases, 
misclassification often happens; also the result may contain a 
significant amount of salt-pepper noise. Thus, object-oriented 
classifications are proposed. Image segmentation is the 
precondition and foundation of object-oriented classification of 
high resolution remotely sensed images and the quality of 
image segmentation greatly influences/affects the accuracy of 
the following processing. 
But efficient segmentation algorithms for high resolution 
remotely sensed images are rarely seen, especially for PolSAR 
data. RGW algorithm presented by White [l) [2] aiming at 
processing common remote sensing images is an edge-detection 
based segmentation algorithm which exploits the cartoon model 
and includes adaptive filter size. But, the RGW segmentation 
results may often contain a large number of small regions, in 
which some segments result from the choice of threshold, and 
some ones are probably anomalies arising from flaws in the 
algorithm [3] [4] . 
In order to overcome the disadvantages of RGW method 
mentioned above, and make it applicable for PolSAR data 
classification, one novel segmentation algorithm based on edge- 
detection is proposed in this paper. 
The algorithm is applied to NASA/JPL AIRSAR L band 
data of Flevoland, Netherlands to test the validity. 
Classification results based on the segmentation result using our 
algorithm demonstrate that the region of Flevoland is well 
classified and the edges are well preserved. 
1.2 Paper Structure 
This paper is organized as follows: Section II presents 
methodology consisting of (a) pre-processing of PolSAR data, 
(b) introduction of edge-detection method used in the algorithm 
proposed in this paper, (c) introduction of region-growing in the 
algorithm, (d) features and improvements of the algorithm 
proposed in this paper. Section III, presents the experiment the 
proposed method using AIRSAR data, and gives classification 
results derived from the experiment. This part also gives brief 
evaluation of the algorithm and the experiment results. Section 
IV is the conclusion. 
2. METHODOLOGY 
First of all a pre-processing should be done to the PolSAR data, 
including decompression, coherence matrix computation and 
* Corresponding author: Jie Yang.