Template Matching-Based Target Recognition Algorithm Development and Verification using SAR Images

Abstract

In this paper, we have developed a target recognition algorithm based on a template matching technique using Synthetic Aperture Radar (SAR) images. For efficient computations, Radon transform-based azimuth estimation algorithm was used with the template matching. MSTAR data set was divided into two groups according to the depression angles, which were a train set and a test set. Template data were generated by rotating and cropping chips which were from MSTAR train set using the azimuth estimation algorithm. Then the template matching process between test data and template data was performed under various conditions. Performance variation according to contrast enhancement preprocessing which is scarce in open literature was also presented. The analysis results show that the target recognition algorithm could be useful for the automatic target recognition using SAR images.

SHIP-YOLO: A Lightweight Synthetic Aperture Radar Ship Detection Model Based on YOLOv8n Algorithm

“…Synthetic aperture radar (SAR) technology, which is characterized by its relative insensitivity to weather and lighting conditions, has been widely applied in ship detection. Traditional SAR ship detection algorithms, such as constant false alarm rate (CFAR) [1], Gaussian modelbased two-parameter CFAR [2], template matching [3], trial detection [4], and wavelet-based [5] detection methods, primarily rely on manually crafted classifiers. Despite their fast computational speeds, these methods exhibit poor detection performance, and the design process of the detection algorithms is intricate.…”

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“…It can monitor relevant sea areas all day and help the development of the modern high-tech war. Traditional SAR ship detection algorithms, such as CFAR [1] (constant false alarm rate), two-parameter CFAR based on the Gaussian model [2], template matching [3], wake detection [4], and detection methods based on wavelet transform [5], mainly rely on artificial classifier design. Although the calculation speed is fast, the detection effect is poor, and the design process of the detection algorithm is complex.…”