A Statistical-Spatial Feature Learning Network for PolSAR Image Classification

Abstract

Polarimetric synthetic aperture radar (PolSAR) image classification plays an important role in the development of remote sensing image interpretation for the rich polarization information. Generative methods learn the statistical distribution characteristic of the scattered echoes data with heavy noise. However, it is tedious to design and solve different generative likelihood functions. Discriminative methods learn image features and classifier in an end-to-end framework whose classification performance is also limited without polarization statistical characteristics. In order to make full use of the statistical characteristic of PolSAR echoes and spatial feature of PolSAR image, a novel real-value hybrid generative/discriminative (HGD) deep network is proposed to learn statistical-spatial feature for PolSAR image classification with the data expressing ability of the generative term and the end-to-end learning ability of the discriminative term. First, a derived replacement form is obtained from the typical Wishart distribution of PolSAR data with an eigenvalue generative term. In this way, the complex-value form of PolSAR data converts into real-value expression, which makes the PolSAR image classification easy to understand and implement in the deep network. Finally, these two terms are integrated from the variational Bayesian theory with an alternative optimization equation derived for PolSAR image classification. It provides a normal framework for PolSAR multifeature learning. Experiments are tested on different PolSAR datasets compared with several state-of-the-art individual generative or discriminative methods. All visual experimental results and accuracy values demonstrate the superiority of the proposed method.