RmSAT-CFAR: fast and accurate target detection in radar images

Nar, Fatih and Okman, Osman Erman and Özgür, Atilla and Çetin, Müjdat (2018) RmSAT-CFAR: fast and accurate target detection in radar images. SoftwareX (SI), 8 . pp. 39-42. ISSN 2352-7110

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As the first step of automatic image interpretation systems, automatic detection of the targets should be accurate and fast. Constant False Alarm Rate (CFAR) is the most popular target detection framework for Synthetic Aperture Radar (SAR) images. For CFAR, modeling of the clutter is crucial since the decision threshold is calculated based on this model. We have developed a new target detection approach in which clutter is modeled using a Rayleigh mixture model that fits well to a variety of high-resolution SAR imagery. For computational efficiency, we use summed area tables (SAT) for computing background statistics. The resulting approach, called RmSAT-CFAR, is a promising general-purpose SAR target detection tool. This paper describes the open-source software for RmSAT-CFAR. Details of RmSAT-CFAR is published in the study named Fast Target Detection in Radar Images using Rayleigh Mixtures and Summed Area Tables. In addition to Rayleigh mixture and SATs, the software also uses tiling and parallelization to obtain faster and scalable results. This software repository also contains open source implementations for following algorithms: (a) Cell Averaging CFAR (CA-CFAR), (b) Automatic Censored CFAR (AC-CFAR), and (c) Adaptive and Fast CFAR (AAF-CFAR).
Item Type: Article
Uncontrolled Keywords: Integral image; Summed area table; CFAR; Target detection; Rayleigh mixture; Adaptive simulated annealing; Parallelization; OpenMP; OpenCV
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Engineering and Natural Sciences > Academic programs > Electronics
Faculty of Engineering and Natural Sciences
Depositing User: Müjdat Çetin
Date Deposited: 15 Feb 2019 14:28
Last Modified: 26 Apr 2022 10:01
URI: https://research.sabanciuniv.edu/id/eprint/36844

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