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

arXiv: https://arxiv.org/abs/1804.04719 · HTML: https://mlsanity.com/paper/1804.04719/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2018.

## Built on by (most cited first, 6 of 6)

- [xView3-SAR: Detecting Dark Fishing Activity Using Synthetic Aperture Radar Imagery](https://mlsanity.com/paper/2206.00897/) (2022)
- [Asynchronous Trajectory Matching-Based Multimodal Maritime Data Fusion for Vessel Traffic Surveillance in Inland Waterways](https://mlsanity.com/paper/2302.11283/) (2023)
- [Deep learning-based object detection of offshore platforms on Sentinel-1 Imagery and the impact of synthetic training data](https://mlsanity.com/paper/2511.04304/) (2025)
- [Offshore oil and gas platform dynamics in the North Sea, Gulf of Mexico, and Persian Gulf: Exploiting the Sentinel-1 archive](https://mlsanity.com/paper/2603.19801/) (2026)
- [Background Debiased SAR Target Recognition via Causal Interventional Regularizer](https://mlsanity.com/paper/2308.15724/) (2023)
- [Maritime object classification with SAR imagery using quantum kernel methods](https://mlsanity.com/paper/2512.11367/) (2025)

## Abstract (arXiv)

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.