Adversarial Reconstruction-Classification Networks for PolSAR Image Classification

Abstract

Polarimetric synthetic aperture radar (PolSAR) image classification has become more and more widely used in recent years. It is well known that PolSAR image classification is a dense prediction problem. The recently proposed fully convolutional networks (FCN) model, which is very good at dealing with the dense prediction problem, has great potential in resolving the task of PolSAR image classification. Nevertheless, for FCN, there are some problems to solve in PolSAR image classification. Fortunately, Li et al. proposed the sliding window fully convolutional networks (SFCN) model to tackle the problems of FCN in PolSAR image classification. However, only when the labeled training sample is sufficient, can SFCN achieve good classification results. To address the above mentioned problem, we propose adversarial reconstruction-classification networks (ARCN), which is based on SFCN and introduces reconstruction-classification networks (RCN) and adversarial training. The merit of our method is threefold: (i) A single composite representation that encodes information for supervised image classification and unsupervised image reconstruction can be constructed; (ii) By introducing adversarial training, the higher-order inconsistencies between the true image and reconstructed image can be detected and revised. Our method can achieve impressive performance in PolSAR image classification with fewer labeled training samples. We have validated its performance by comparing it against several state-of-the-art methods. Experimental results obtained by classifying three PolSAR images demonstrate the efficiency of the proposed method.