Automatic Target Recognition on Synthetic Aperture Radar Imagery: A Survey

TL;DR

This survey reviews and compares current SAR ATR architectures using the MSTAR dataset, highlighting their strengths, weaknesses, and future research directions in military target recognition.

Contribution

It provides a comprehensive taxonomy and analysis of SAR ATR methods, identifying gaps and proposing future research directions.

Findings

Different SAR ATR architectures have varying strengths and weaknesses.

The MSTAR dataset has limitations that affect benchmarking.

Future research should address dataset weaknesses and explore new methodologies.

Abstract

Automatic Target Recognition (ATR) for military applications is one of the core processes towards enhancing intelligencer and autonomously operating military platforms. Spurred by this and given that Synthetic Aperture Radar (SAR) presents several advantages over its counterpart data domains, this paper surveys and assesses current SAR ATR architectures that employ the most popular dataset for the SAR domain, namely the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset. Based on the current methodology trends, we propose a taxonomy for the SAR ATR architectures, along with a direct comparison of the strengths and weaknesses of each method under both standard and extended operational conditions. Additionally, despite MSTAR being the standard SAR ATR benchmarking dataset we also highlight its weaknesses and suggest future research directions.