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Data Processing: Data Fusion
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Classification of Polarimetric SAR imagerary based on Target Decomposition and neural
network classifier
CHEN Jin Song 1,2 , SHAO Yun 1 , LIN Hui 2
(1.Institute of Remote Sensing Applications, Chinese Academy of Science,
Beijing 100101, China)
(2. Joint laboratory for geoinformation science,Esther Li Bldg.,Rm615,Chung Chi college
The Chinese university of Hong Kong, Shatin,N.T., Hong Kong)
Email: chenjinsong@cuhk.edu.hk
Abstract
Based on SIR-C data of He Tian prefecture in Xinjiang of China, in this paper we first
use Cloude’s target decomposition theory to decompose the SIR-C data into three no-related
scattering components: an odd number of reflections, aneven number reflections, and a cross-polarized
scattering power, which represent different scattering mechanism of ground objects. And
then we employ neural networks classifier to classify the SIR-C images using the decomposed
images with polarimetric synthesized total power image and scattering entropy . The decomposition
result shows that the decomposed three scattering components could reflect the correct scattering
feature . The classification result shows that the method can effectively extract information of land
cover , achieve the better classification accuracy of ground objects and improve the ability of
polarimetric SAR to monitor the land use and cover.
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