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19 February 2018 Gaussian mixture models-based ship target recognition algorithm in remote sensing infrared images
Shoukui Yao, Xiaojuan Qin
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Proceedings Volume 10608, MIPPR 2017: Automatic Target Recognition and Navigation; 1060808 (2018) https://doi.org/10.1117/12.2283463
Event: Tenth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2017), 2017, Xiangyang, China
Abstract
Since the resolution of remote sensing infrared images is low, the features of ship targets become unstable. The issue of how to recognize ships with fuzzy features is an open problem. In this paper, we propose a novel ship target recognition algorithm based on Gaussian mixture models (GMMs). In the proposed algorithm, there are mainly two steps. At the first step, the Hu moments of these ship target images are calculated, and the GMMs are trained on the moment features of ships. At the second step, the moment feature of each ship image is assigned to the trained GMMs for recognition. Because of the scale, rotation, translation invariance property of Hu moments and the power feature-space description ability of GMMs, the GMMs-based ship target recognition algorithm can recognize ship reliably. Experimental results of a large simulating image set show that our approach is effective in distinguishing different ship types, and obtains a satisfactory ship recognition performance.
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Shoukui Yao and Xiaojuan Qin "Gaussian mixture models-based ship target recognition algorithm in remote sensing infrared images", Proc. SPIE 10608, MIPPR 2017: Automatic Target Recognition and Navigation, 1060808 (19 February 2018); https://doi.org/10.1117/12.2283463
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KEYWORDS
Detection and tracking algorithms
Infrared imaging
Remote sensing
Evolutionary algorithms
Target recognition
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