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Deep Learning Meets SAR: Concepts, Models, Pitfalls, and Perspectives

Zhu, Xiaoxiang and Montazeri, Sina and Ali, Mohsin and Hua, Yuansheng and Wang, Yuanyuan and Mou, Lichao and Shi, Yilei and Xu, Feng and Bamler, Richard (2021) Deep Learning Meets SAR: Concepts, Models, Pitfalls, and Perspectives. IEEE Geoscience and Remote Sensing Magazine (GRSM). IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/MGRS.2020.3046356. ISSN 2168-6831. (In Press)

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Official URL: https://ieeexplore.ieee.org/document/9351574

Abstract

Deep learning in remote sensing has become an international hype, but it is mostly limited to the evaluation of optical data. Although deep learning has been introduced in Synthetic Aperture Radar (SAR) data processing, despite successful first attempts, its huge potential remains locked. In this paper, we provide an introduction to the most relevant deep learning models and concepts, point out possible pitfalls by analyzing special characteristics of SAR data, review the state-of-the-art of deep learning applied to SAR in depth, summarize available benchmarks, and recommend some important future research directions. With this effort, we hope to stimulate more research in this interesting yet under-exploited research field and to pave the way for use of deep learning in big SAR data processing workflows.

Item URL in elib:https://elib.dlr.de/141023/
Document Type:Article
Title:Deep Learning Meets SAR: Concepts, Models, Pitfalls, and Perspectives
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Zhu, XiaoxiangUNSPECIFIEDUNSPECIFIED
Montazeri, SinaUNSPECIFIEDUNSPECIFIED
Ali, MohsinUNSPECIFIEDUNSPECIFIED
Hua, YuanshengUNSPECIFIEDUNSPECIFIED
Wang, YuanyuanUNSPECIFIEDUNSPECIFIED
Mou, LichaoUNSPECIFIEDUNSPECIFIED
Shi, YileiUNSPECIFIEDUNSPECIFIED
Xu, FengUNSPECIFIEDUNSPECIFIED
Bamler, RichardUNSPECIFIEDUNSPECIFIED
Date:2021
Journal or Publication Title:IEEE Geoscience and Remote Sensing Magazine (GRSM)
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
DOI :10.1109/MGRS.2020.3046356
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:2168-6831
Status:In Press
Keywords:deep learning, SAR, perspectives
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 - Remote Sensing and Geo Research, R - SAR methods
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Remote Sensing Technology Institute > Leitungsbereich MF
Deposited By: Bratasanu, Ion-Dragos
Deposited On:19 Feb 2021 19:10
Last Modified:19 Feb 2021 19:10

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