Full polarization SAR image classification using deep learning with shallow feature

Debo Li, Yu Gu, Shuiping Gou, Licheng Jiao

Abstract: The classification of the POL-SAR image become more and more important with the development of the polarization of synthetic aperture radar system. Generally, the classification of POL-SAR images are based on polarization feature, such as support vector machine (SVM), Wishart clustering and other methods. Specifically, some ground objects usually have some weak scattering characteristics which cannot obtain good results by only using the traditional classification based on polarization features. So, the deep learning based on T matrix is used to mine the powerful feature of SAR data. In order to speed up computation and improve classification accuracy, a classification of full-polarization SAR images based on Deep Learning with Shallow features is proposed in this paper. The proposed method can get better classification for those weak scatter objects than those methods only using polarization features.