Semantic Scholar Open Access 2020 287 sitasi

Deep Learning Meets SAR: Concepts, models, pitfalls, and perspectives

Xiaoxiang Zhu S. Montazeri Mohsin Ali Yuansheng Hua Yuanyuan Wang +4 lainnya

Lihat Sumber DOI

Abstrak

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.

Topik & Kata Kunci

Computer Science Engineering Mathematics

Penulis (9)

Xiaoxiang Zhu

S. Montazeri

Mohsin Ali

Yuansheng Hua

Yuanyuan Wang

Lichao Mou

Yilei Shi

Feng Xu

R. Bamler

Format Sitasi

APA MLA BibTeX

Zhu, X., Montazeri, S., Ali, M., Hua, Y., Wang, Y., Mou, L. et al. (2020). Deep Learning Meets SAR: Concepts, models, pitfalls, and perspectives. https://doi.org/10.1109/MGRS.2020.3046356

Akses Cepat

Lihat di Sumber doi.org/10.1109/MGRS.2020.3046356

Informasi Jurnal

Tahun Terbit: 2020
Bahasa: en
Total Sitasi: 287×
Sumber Database: Semantic Scholar
DOI: 10.1109/MGRS.2020.3046356
Akses: Open Access ✓