Abstract
In order to make up the shortcomings of the traditional CFAR detection algorithm, a ship target detection algorithm based on information theory and Harris corner detection for SAR images is proposed in this paper. Firstly, the SAR image is pretreated, and next, it is divided into superpixel patches by using the improved SLIC superpixel generation algorithm. Then, the self-information value of the superpixel patches is calculated and the threshold T1 is set to select the candidate superpixel patches. And then, the extended neighborhood weighted information entropy growth rate threshold T2 is set to eliminate false alarm detection results of the candidate superpixel patches. Finally, the Harris corner detection algorithm is used to process the detection result, the number of the corner threshold T3 is set to filter out the false alarm patches, and the final SAR image target detection result is obtained. The effectiveness and superiority of the proposed algorithm are verified by comparing the proposed method with the results of CFAR detection algorithm combining with morphological processing algorithm and information theory combining with morphological processing algorithm on the experimental high-resolution ship SAR images.
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Wang, H., Ran, Y., Liu, S., Deng, Y., Su, D. (2020). Ship Target Detection in High-Resolution SAR Images Based on Information Theory and Harris Corner Detection. In: Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2018. Lecture Notes in Electrical Engineering, vol 516. Springer, Singapore. https://doi.org/10.1007/978-981-13-6504-1_83
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DOI: https://doi.org/10.1007/978-981-13-6504-1_83
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