Automatic Target Recognition in Synthetic Aperture Radar Imagery: A State-of-the-Art Review

By Khalid El-Darymli, Eric W. Gill, Peter F. McGuire, Desmond Power, Cecilia R. Moloney. IEEE Access, 2016

336 citations recorded by the source corpus.

The purpose of this paper is to survey and assess the state-of-the-art in automatic target recognition for synthetic aperture radar imagery (SAR-ATR). The aim is not to develop an exhaustive survey of the voluminous literature, but rather to capture in one place the various approaches for implementing the SAR-ATR system. This paper is meant to be as self-contained as possible, and it approaches the SAR-ATR problem from a holistic end-to-end perspective. A brief overview for the breadth of the SAR-ATR challenges is conducted. This is couched in terms of a single-channel SAR, and it is extendable to multi-channel SAR systems. Stages pertinent to the basic SAR-ATR system structure are defined, and the motivations of the requirements and constraints on the system constituents are addressed. For each stage in the SAR-ATR processing chain, a taxonomization methodology for surveying the numerous methods published in the open literature is proposed. Carefully selected works from the literature are presented under the taxa proposed. Novel comparisons, discussions, and comments are pinpointed throughout this paper. A two-fold benchmarking scheme for evaluating existing SAR-ATR systems and motivating new system designs is proposed. The scheme is applied to the works surveyed in this paper. Finally, a discussion is presented in which various interrelated issues, such as standard operating conditions, extended operating conditions, and target-model design, are addressed. This paper is a contribution toward fulfilling an objective of end-to-end SAR-ATR system design.

Subfields: Aerospace Engineering, Computational Mechanics

View Paper: https://www.semanticscholar.org/paper/90782eb65a6474c9e49273aa090628ce6d117b69

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Citations in the corpus

• Automatic Ship Detection Based on RetinaNet Using Multi-Resolution Gaofen-3 Imagery (2019, background): “Targets on SAR imagery are sensitive to pose and configuration [8,24].”
• A SAR Dataset of Ship Detection for Deep Learning under Complex Backgrounds (2019, methodology): “In addition, these methods are typically dependent on the statistical distribution of sea clutter [1,2,11], leading to poor robustness for new SAR imagery.”
• Transfer Learning with Deep Convolutional Neural Network for SAR Target Classification with Limited Labeled Data (2017, methodology): “The first two stages that are also known as prescreening and discrimination together generate the focus-of-attention (FOA) module [1].”