PolSAR Image Classification Using Hybrid Conditional Random Fields Model Based on Complex-Valued 3-D CNN

Abstract

In this article, we propose a hybrid conditional random field model based on complex-valued 3-D convolutional neural network (C3NN), named as HCRF-C3NN, for polarimetric synthetic aperture radar (PolSAR) image classification. HCRF-C3NN combines the modeling power of random fields with the representation-learning ability of deep learning model for PolSAR image. By C3NN, HCRF-C3NN exploits deep amplitude and phase information of PolSAR data and, thus, measures the class probabilities in the framework of random fields. Additionally, HCRF-C3NN captures the spatial label interactions effectively in PolSAR image classification by the following work: 1) Based on the C3NN-based class probabilities, the relative entropy of class distributions is derived to describe the local structure and, thus, regulates the pairwise label interactions, which enhances the accuracy of edge location in the classification; 2) a product-of-expert potential is introduced to enforce label consistency, and, thus, HCRF-C3NN is more robust against speckle of PolSAR image and achieves smoother classification within the consistent class region. At last, to capture PolSAR image information in a more complete manner, the deep features and PolSAR scattering statistics are integrated into HCRF-C3NN based on Bayesian fusion. In this way, HCRF-C3NN effectively explores spatial correlation, scattering statistics, and deep features of PolSAR data in the framework of random fields. The experimental results demonstrate the superiority of HCRF-C3NN over the recent deep learning models for PolSAR image classification.