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Proceedings Paper

Hyperspectral target detection based on improved automatic morphological endmember extraction method
Author(s): Xu-guang Sun; Jing-ju Cai; Zhi-yong Xu; Jian-lin Zhang
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Paper Abstract

At present, commonly endmember extraction of hyperspectral is mainly concentrated in the spectral region. Because the spatial information is not used enough, the endmember extraction is not precise which can lead to a bad result of mixed pixel decomposition and hyperspectral target detection. Actually, the distribution of endmembers in space has a certain shape and aggregation. By making use of these information we can extract more precise endmembers. Automatic morphological endmember extraction technology can make full use of abundant spectral information and spatial information. This paper based on the existed automatic morphological algorithm, presents a method in combination with maximum distance for morphological endmember extraction to solve the influence of spectral variations, which effectively extracts different classes of endmember curves. Based on the theory of orthogonal subspace projection, the authors propose an improved constrained energy minimization (CEM) algorithm, achieve better hyperspectral target detection results.

Paper Details

Date Published: 15 October 2012
PDF: 9 pages
Proc. SPIE 8415, 6th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Large Mirrors and Telescopes, 841518 (15 October 2012); doi: 10.1117/12.976015
Show Author Affiliations
Xu-guang Sun, Graduate Univ. of the Chinese Academy of Sciences (China)
Institute of Optics and Electronics (China)
Jing-ju Cai, Institute of Optics and Electronics (China)
Zhi-yong Xu, Institute of Optics and Electronics (China)
Jian-lin Zhang, Institute of Optics and Electronics (China)


Published in SPIE Proceedings Vol. 8415:
6th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Large Mirrors and Telescopes
Wenhan Jiang; Myung K. Cho; Fan Wu, Editor(s)

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