Deep Learning Meets SAR

TL;DR

This paper reviews the application of deep learning to Synthetic Aperture Radar (SAR) data, highlighting challenges, current state-of-the-art, benchmarks, and future research directions to unlock its full potential.

Contribution

It provides a comprehensive overview of deep learning models for SAR, analyzes data-specific pitfalls, and suggests future research paths to advance the field.

Findings

Deep learning has been underutilized in SAR data processing.

SAR data presents unique challenges for deep learning models.

The paper summarizes benchmarks and proposes future research directions.

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.