Classification of polarimetric synthetic aperture radar images based on multilayer Wishart-restricted Boltzmann machine

Terrain classification is an important application for polarimetric synthetic radar (PolSAR) image processing. Inspired by the popular deep belief network (DBN), a PolSAR classification method is proposed, which is called multilayer Wishart restricted Boltzmann machine (MWRBM). For PolSAR data, the traditional DBN is limited by binary value distribution, which is not suitable for PolSAR image classification. Therefore, according to the statistical distribution of PolSAR data, a new type of Wishart-restricted Boltzmann machine is proposed. An MWRBM, as one of the deep learning models, is proposed for PolSAR image classification. For improving the classification result, the labeled samples are used to fine-tune the parameters of the proposed deep model. Finally, two real PolSAR datasets are tested to verify the effectiveness of the proposed method. Experimental results demonstrate that the proposed method is very effective and compare favorably to the state-of-the-art methods. (C) 2020 Society of Photo-Optical Instrumentation Engineers (SPIE)