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-1Dual-polarized SAR data and the corresponding scattering mechanisms...
| Published in: | 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS |
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| Main Authors: | , , |
| Format: | Conference Object |
| Language: | unknown |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://elib.dlr.de/142805/ https://igarss2021.com/view_paper.php?PaperNum=2617 |
| Summary: | 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. |
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