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

Huang, Zhongling and Dumitru, Corneliu Octavian and Ren, Jun (2021) Physics-aware feature learning of SAR images with deep neural networks: A case study. In: International Geoscience and Remote Sensing Symposium (IGARSS), pp. 1-4. IGARSS 2021, 12.-16. July 2021, Brussels, Belgium.

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Official URL: https://igarss2021.com/view_paper.php?PaperNum=2617

Abstract

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.

Item URL in elib:https://elib.dlr.de/142805/
Document Type:Conference or Workshop Item (Speech)
Title:Physics-aware feature learning of SAR images with deep neural networks: A case study
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Huang, Zhonglinghuangzhongling15 (at) mails.ucas.ac.cnUNSPECIFIED
Dumitru, Corneliu OctavianCorneliu.Dumitru (at) dlr.deUNSPECIFIED
Ren, JunUNSPECIFIEDUNSPECIFIED
Date:July 2021
Journal or Publication Title:International Geoscience and Remote Sensing Symposium (IGARSS)
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:No
Page Range:pp. 1-4
Status:Published
Keywords:Physics-guided learning, feature learning, SAR image understanding, sea-ice classification, deep learning
Event Title:IGARSS 2021
Event Location:Brussels, Belgium
Event Type:international Conference
Event Dates:12.-16. July 2021
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Artificial Intelligence
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Dumitru, Corneliu Octavian
Deposited On:24 Jun 2021 09:50
Last Modified:09 Aug 2021 13:30

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