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Proceedings Paper

Surface ship target detection in hyperspectral images based on improved variance minimum algorithm
Author(s): Zhengzhou Wang; Qinye Yin; Hongguang Li; Bingliang Hu
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Paper Abstract

In order to realize the effective detection of surface structure targets in hyperspectral images, an improved target detection algorithm was proposed in this paper presents to solve the CEM algorithm problems which the large object extraction efficiency is low .First, the image was preprocessed, including end-member extraction, SAM classification. Second, after the ship pixels were subtracted from all pixels, the correlation matrix of pure background pixels was constructed to detect ship target. Third, the biggest write region was found as sea region by mathematical morphology. Finally, the false target pixels were removed from all target pixels using the characteristics which ship targets were surrounded in seawater, so the final ship targets were selected in the end. Experimental results show that the final max ratio between the energy of detection target and the energy of background increased greatly, the target signal is enhanced and the background signal is suppressed by the improved algorithm.

Paper Details

Date Published: 29 August 2016
PDF: 7 pages
Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100330R (29 August 2016); doi: 10.1117/12.2243872
Show Author Affiliations
Zhengzhou Wang, Xi'an Jiaotong Univ. (China)
Univ. of Chinese Academy of Sciences (China)
Xi'an Institute of Optics and Precision Mechanics (China)
Qinye Yin, Univ. of Chinese Academy of Sciences (China)
Hongguang Li, Xi'an Institute of Optics and Precision Mechanics (China)
Bingliang Hu, Xi'an Institute of Optics and Precision Mechanics (China)


Published in SPIE Proceedings Vol. 10033:
Eighth International Conference on Digital Image Processing (ICDIP 2016)
Charles M. Falco; Xudong Jiang, Editor(s)

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