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Hyperspectral target detection based on improved automatic morphological endmember extraction method
Sun, Xu-Guang1,2; Cai, Jing-Ju2; Xu, Zhi-Yong2; Zhang, Jian-Lin2; Sun, X.-G.
Volume8415
Pages841518
2012
Language英语
ISSN0277786X
DOI10.1117/12.976015
Indexed ByEi
Subtype会议论文
AbstractAt 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. © 2012 SPIE.; 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. © 2012 SPIE.
Conference NameProceedings of SPIE: 6th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Large Mirrors and Telescopes
Conference Date2012
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type会议论文
Identifierhttp://ir.ioe.ac.cn/handle/181551/7690
Collection光电探测与信号处理研究室(五室)
Corresponding AuthorSun, X.-G.
Affiliation1. Graduate University, Chinese Academy of Science, Beijing, 100190, China
2. Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu, 610209, China
Recommended Citation
GB/T 7714
Sun, Xu-Guang,Cai, Jing-Ju,Xu, Zhi-Yong,et al. Hyperspectral target detection based on improved automatic morphological endmember extraction method[C],2012:841518.
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