Neural Networks Classification of Quad-polarization SAR Data Based on Target Decomposition ABSTRACT
基于目标分解理论的全极化SAR图像神经网络分类方法

SIR-C is the first spaceborne imaging Radar system with multi-wavelength and quad-polarization developed by joint effort of The U.S, Italy and Germany. Polarization SAR can measures the scattering matrix of each pixel on ground and synthesizes the image at given orientation and ellipticity angle , including linear and elliptical polarization. It has many advantages over single or multi-polarization SAR in detecting objects, identifying targets and extracting geometric structure of ground targets. During recent years, theoretical modeling and field experiments have established the fundamentals of active microwave remote sensing as an important tool in determining physical properties of ground objects. But different ground targets often have the same polarization signal characteristics because of the complexity of the distribution of the targets , which leads to wrong interpretation of the images and identification of the targets. Besides, relatively high correlation of the synthesized polarized images often lead to poor accuracy of classification. Based on SIR-C data of He Tian prefecture in Xinjiang of China, we use target decomposition theory to decompose the data into three no-related scattering components: an odd number of reflections, an even number reflections, and a cross-polarized scattering power, which represent different scattering mechanism of different objects. This decomposition technique allows us to obtain the estimation of single and double reflection components of backscattering coefficients for VV and HH polarization .They greatly improve the correctness of identification of ground objects. And what is more, the three components are non-correlated., which provides richer data resource. This paper employed neural networks classifier to classify the SAR images by combining them with polarimetric synthesized SAR power image. The decomposition result shows that the decomposed three scattering components 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 SAR to monitor the land use and cover.