Target detection in synthetic aperture radar imagery: a state-of-the-art survey

TL;DR

This survey comprehensively reviews SAR target detection methods, proposing a taxonomy, comparing representative techniques, and discussing CFAR detection from signal processing and pattern recognition perspectives to guide objective design.

Contribution

It introduces a taxonomy for SAR target detection methods, compares key approaches, and offers novel insights into CFAR detection from multiple perspectives.

Findings

CFAR can be viewed as a finite impulse response band-pass filter.

CFAR functions as a suboptimal one-class classifier in pattern recognition.

The paper provides a framework for objective SAR target detection design.

Abstract

Target detection is the front-end stage in any automatic target recognition system for synthetic aperture radar (SAR) imagery (SAR-ATR). The efficacy of the detector directly impacts the succeeding stages in the SAR-ATR processing chain. There are numerous methods reported in the literature for implementing the detector. We offer an umbrella under which the various research activities in the field are broadly probed and taxonomized. First, a taxonomy for the various detection methods is proposed. Second, the underlying assumptions for different implementation strategies are overviewed. Third, a tabular comparison between careful selections of representative examples is introduced. Finally, a novel discussion is presented, wherein the issues covered include suitability of SAR data models, understanding the multiplicative SAR data models, and two unique perspectives on constant false alarm rate (CFAR) detection: signal processing and pattern recognition. From a signal processing perspective, CFAR is shown to be a finite impulse response band-pass filter. From a statistical pattern recognition perspective, CFAR is shown to be a suboptimal one-class classifier: a Euclidian distance classifier and a quadratic discriminant with a missing term for one-parameter and two-parameter CFAR, respectively. We make a contribution toward enabling an objective design and implementation for target detection in SAR imagery.