Literature Review on Rotating Target Detection in SAR Images Based on Deep Learning in Complex Background

Jing Wang; Bin Wu; Hongying Zhang

doi:10.62051/ijcsit.v5n3.14

Authors

Jing Wang
Bin Wu
Hongying Zhang

DOI:

https://doi.org/10.62051/ijcsit.v5n3.14

Keywords:

Rotated object detection, Complex background, Synthetic Aperture Radar, Deep learning

Abstract

The detection of targets in Synthetic Aperture Radar (SAR) images holds paramount significance for both military and civilian applications, furnishing critical informational support for target surveillance. Deep learning, owing to its robust learning capabilities, has emerged as a favorable solution for addressing the intricacies of SAR image rotating target detection in complex backgrounds. However, numerous challenges persist. The mitigation of speckles in SAR images and the detection of rotating targets amidst dense and small objects within intricate backgrounds constitute pivotal and challenging issues in this domain. This paper systematically reviews algorithms pertaining to these challenges, contrasts the performance structures, advantages, and drawbacks of different algorithms, and ultimately delves into prospective research directions.

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