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浙江大学学报(工学版)
J4  2012, Vol. 46 Issue (10): 1857-1865    DOI: 10.3785/j.issn.1008-973X.2012.10.019
    
Endmember extraction method for subpixel materials
in hyperspectral imagery
CUI Jian-tao1, LI Xiao-run1, ZHAO Liao-ying2
1. College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China; 2. Institute of Computer
Application Technology, Hangzhou Dianzi University, Hangzhou 310018, China
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Abstract  

An endmember extraction algorithm based on the theory of convex geometry and partial nonnegative matrix factorization was proposed in order to solve the problem of extracting the endmembers of subpixel materials in hyperspectral imagery. The dewhitening endmember extraction algorithm based on orthogonal bases of subspace and the similarity comparison were applied to obtain the endmembers containing pure pixels, and the reconstruction error (RE) for every pixel was calculated only with these endmembers. A partial nonnegative matrix factorization algorithm was implemented with the pixels whose REs are greater than the preset threshold, and the endmembers of the subpixel materials were achieved. Experimental results demonstrate that the algorithm can remedy the weakness of the traditional methods and availably retrieve endmembers of the subpixel materials.

Published: 01 October 2012
CLC:  TP 751  
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Cite this article:

CUI Jian-tao, LI Xiao-run, ZHAO Liao-ying. Endmember extraction method for subpixel materials
in hyperspectral imagery. J4, 2012, 46(10): 1857-1865.

URL:

http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2012.10.019     OR     http://www.zjujournals.com/eng/Y2012/V46/I10/1857


高光谱图像亚像元级地物端元提取方法

针对高光谱图像中以亚像元形式存在的地物的端元光谱提取问题,提出凸面几何理论和部分非负矩阵分解相结合的端元提取方法.通过去噪的正交基子空间投影方法和相似度比较获得原始图像中的纯像元端元,利用纯像元端元光谱对图像逐点求取丰度和重构误差,对误差大于设定阈值的像素集合进行部分非负矩阵分解,求得亚像元级地物的端元光谱.实验结果表明,该端元提取方法能够弥补传统方法的不足,从而实现对亚像元级地物端元光谱的有效提取.

［1］ 童庆禧,张兵,郑兰芬.高光谱遥感的多学科应用［M］.北京：电子工业出版社,2006: 262.
［2］ BOARDMAN J W, KRUSE F A, GREEN R O. Mapping target signatures via partial unmixing of AVIRIS data: in summaries ［C］∥ 5th JPL Airborne Earth Science Workshop. Pasadena: JPL, 1995: 23-26.
［3］ WINTER M E. NFINDR: an algorithm for fast autonomous spectral endmember determination in hyperspectral data ［C］∥ Proceedings of the SPIE conference on Imaging Spectrometry V. Washington: SPIE, 1999: 266-275.
［4］ IFARRAGUEERRI A, CHANG C I. Multispectral and hyperspectral image analysis with convex cones ［J］. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(2): 756-770.
［5］ NASCIMENTO J M P, DIAS J M B. Vertex component analysis: a fast algorithm to unmix hyperspectral data ［J］. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43 (4): 898.
［6］ BERMAN M, KIIVERI H, LAGERSTORM R, et al. Ice: a statistical approach to identifying endmembers in hyperspectral images ［J］. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(10): 2085-2095.
［7］ MIAO Lidan, QI Hairong. Endmember extraction from highly mixed data using minimum volume constrained nonnegative matrix factorization ［J］. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(3): 765-777.
［8］ PAUCA V P, PIPER J, PLEMMONS R J. Nonnegative matrix factorization for spectral data analysis ［J］. Linear Algebra and Applications, 2006, 416 (1): 29-47.
［9］ LEE D D, SEUNG H S. Algorithm for nonnegative matrix factorization ［J］. Advances in Neural Information Processing Systems, 2001, 3(13): 556-562.
［10］ ROGGE D M, RIVARD B, ZHANG Jinkai, et al. Iterative spectral unmixing for optimizing perpixel endmember sets ［J］. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(12): 3725-3736.
［11］ 童庆禧,张兵,郑兰芬.高光谱遥感：原理,技术与应用［M］.北京：高等教育出版社, 2006: 55.
［12］ TAO Xuetao, WANG Bin, ZHANG Liming, et al. A new endmember extraction algorithm based on orthogonal bases of subspace formed by endmembers ［C］∥ IEEE International Geoscience and Remote Sensing Symposium. Barcelona: IEEE, 2007: 2006-2009.
［13］ JOLLIFFE L T. Principal component analysis ［M］. New York: Springer, 1986: 45.
［14］ GREEN A, BERMAN M, SWITZER P, et al. A transformation for ordering multispectral data in terms of image quality with implications for noise removal ［J］. IEEE Transactions on Geoscience and Remote Sensing, 1994, 26(1): 65-74.
［15］ SCHARF L L. Statistical signal processing, detection estimation and time series analysis ［M］. Reading, MA: Wesley, 1991.
［16］ KESHAVA N, MUSTARD J F. Spectral unmixing ［J］. IEEE Signal Processing Magazine, 2002, 19(3): 44-57.
［17］ SHIMABUKURO Y E, SMITH J A. The leastsquare mixing models to generate fraction images derived from remote sensing multispectral data ［J］. IEEE Transactions on Geoscience and Remote Sensing, 1991, 29（1）: 16-20.
［18］ 贾森,钱沄涛,纪震,等.基于光谱和空间特性的高光谱解混方法［J］.深圳大学学报：理工版,2009,26(3): 262267.
JIA Sen, QIAN Yuntao, JI Zhen, et al. Spectral and spatial characterbased hyperspectral unmixing ［J］. Journal of Shenzhen University: Science and Engineering, 2009, 26(3): 262-267.
［19］ CLARK R N, SWAYZE G A, GALLAGHER A, et al. The U. S. geological survey digital spectral library: version 1: 02 to 3.0 microns ［R］. Virginia: United States Geological Survery, 1993: 93-592.
［20］ 贾森.非监督的高光谱图像解混技术研究［D］.杭州：浙江大学,2007.
JIA Sen. Unsupervised hyperspectral unmixing theory and techniques ［D］. Hangzhou: Zhejiang University, 2007.
［21］ SWAYZE G A, CLARK R L, SUTLEY S, et al. Groundtruthing AVIRIS mineral mapping at cuprite, Nevada ［C］∥ Summaries of the 3rd Annual JPL Airborne Geoscience Workshop. Pasadena: ［s.n.］, 1992: 47-49.
［22］ CHANG C I, DU Q. Estimation of number of the cuprite mining district, southwestern Nevada: an integrated geological and geophysical approach ［D］. Boulder: University of Colorado, 1997.
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