Physics-aware feature learning of SAR images with deep neural networks: A case study

• Published in: 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS
• Main Authors: Huang, Zhongling, Dumitru, Corneliu Octavian, Ren, Jun
• Format: Conference Object
• Language: unknown
• Published: 2021
• Subjects: EO Data Science; Sea ice
• Online Access: https://elib.dlr.de/142805/; https://igarss2021.com/view_paper.php?PaperNum=2617

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-1Dual-polarized SAR data and the corresponding scattering mechanisms derived from H/αWishart 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.