PHYSICS-AWARE FEATURE LEARNING OF SAR IMAGES WITH DEEP NEURAL NETWORKS: A CASE STUDY

This paper proposes a novel unsupervised learning method to learn discriminative physics-aware features of Synthetic Aperture Radar images with deep neural networks. We conduct a case study of sea-ice classification using Sentinel-1 Dual-polarized SAR data and the corresponding scattering mechanisms derived from H/ff Wishart classification. The scattering mechanisms are encoded as a combination of topics for each SAR image as physics attributes, which guide the deep convolutional neural network to learn physics-aware features automatically. A novel objective function is designed to demonstrate how to conduct the physics-guided learning processing. The experiments show the proposed method can learn discriminative features from SAR images without labeled data, which can achieve a comparable classification result with supervised CNN learning.