19 February 2018 Low-resolution ship detection from high-altitude aerial images
Author Affiliations +
Proceedings Volume 10608, MIPPR 2017: Automatic Target Recognition and Navigation; 1060805 (2018) https://doi.org/10.1117/12.2282780
Event: Tenth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2017), 2017, Xiangyang, China
Ship detection from optical images taken by high-altitude aircrafts such as unmanned long-endurance airships and unmanned aerial vehicles has broad applications in marine fishery management, ship monitoring and vessel salvage. However, the major challenge is the limited capability of information processing on unmanned high-altitude platforms. Furthermore, in order to guarantee the wide detection range, unmanned aircrafts generally cruise at high altitudes, resulting in imagery with low-resolution targets and strong clutters suffered by heavy clouds. In this paper, we propose a low-resolution ship detection method to extract ships from these high-altitude optical images. Inspired by a recent research on visual saliency detection indicating that small salient signals could be well detected by a gradient enhancement operation combined with Gaussian smoothing, we propose the facet kernel filtering to rapidly suppress cluttered backgrounds and delineate candidate target regions from the sea surface. Then, the principal component analysis (PCA) is used to compute the orientation of the target axis, followed by a simplified histogram of oriented gradient (HOG) descriptor to characterize the ship shape property. Finally, support vector machine (SVM) is applied to discriminate real targets and false alarms. Experimental results show that the proposed method actually has high efficiency in low-resolution ship detection.
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Shengxiang Qi, Shengxiang Qi, Jianmin Wu, Jianmin Wu, Qing Zhou, Qing Zhou, Minyang Kang, Minyang Kang, } "Low-resolution ship detection from high-altitude aerial images", Proc. SPIE 10608, MIPPR 2017: Automatic Target Recognition and Navigation, 1060805 (19 February 2018); doi: 10.1117/12.2282780; https://doi.org/10.1117/12.2282780

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