Deep Learning Meets SAR: Concepts, models, pitfalls, and perspectives
By Xiaoxiang Zhu, Sina Montazeri, Mohsin Ali, Yuansheng Hua, Yuanyuan Wang, Lichao Mou, Yilei Shi, Feng Xu, Richard Bamler. IEEE Geoscience and Remote Sensing Magazine, 2020
314 citations recorded by the source corpus.
Deep learning in remote sensing has received considerable 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 article, 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, summarize available benchmarks, and recommend some important future research directions. With this effort, we hope to stimulate more research in this interesting yet underexploited field and to pave the way for the use of deep learning in big SAR data processing workflows.
Subfields: Aerospace Engineering, Computational Mechanics
View Paper: https://www.semanticscholar.org/paper/18bc41282e2879843efc5409fdec745bc9f69152
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In Aerospace Engineering
In Computational Mechanics
Citations in the corpus
- Self-Supervised Learning in Remote Sensing: A review (2022, background): “We refer the interested reader to the numerous surveys on machine learning and deep learning in remote sensing [8], [239]–[244] for more details about common downstream tasks and available datasets.”