Multi-aspect Polarimetric SAR Image Scattering Feature Information Coding and SVM Classification

Multi-aspect polarimetric SAR images is rich of anisotropic scattering signature information for terrain classification. By using an anisotropic scattering detection model, we propose a new sequence-to-sequence signature merging framework combining aspect signatures with polarimetric decomposition signatures in this paper. First of all, the anisotropic scattering centers are detected to generate a new sequence images from the original sub-aperture image sequence. In addition, there are three polarimetric decomposition models including Yamaguchi four-component decomposition, Yang decomposition and H/A/Alpha decomposition are performed on the new sequence images to generate a series of signature curves. The framework also includes the normalization of the curve, the quantization coding and transcoding. At last, a support vector basis (SVM) classification results was used to analysis. Our algorithm was validated and applied to a set of airborne P-band fully polarimetric circular SAR data acquired by the Institute of Electronics, Chinese Academy of Science.