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J4  2013, Vol. 47 Issue (9): 1517-1523    DOI: 10.3785/j.issn.1008-973X.2013.09.002
计算机技术,无线电电子学     
基于空间像素纯度指数的端元提取算法
崔建涛1,王晶1,厉小润1,赵辽英2
1.浙江大学 电气工程学院,浙江 杭州 310027;2.杭州电子科技大学 计算机应用技术研究所,浙江 杭州 310018
Endmember extraction algorithm based on spatial pixel purity index
CUI Jian-tao1, WANG Jing1,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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摘要:

为了减小光谱变化以及异常像素点对端元提取结果的影响,根据局部区域内纯像元和混合像元光谱特征的不同,提出一种基于空间像素纯度指数的端元提取算法.将光谱角距离和欧氏距离加权相加作为新的混合距离测度;采用固定大小的邻域窗口计算图像中所有像素的空间像素纯度指数,在此基础上,根据光谱角距离测度和设定的端元光谱区分性阈值依次搜索端元.仿真数据和真实高光谱图像实验结果表明:该算法能够准确地提取图像中的端元,并且精度高于其他一些端元提取算法.

Abstract:

In order to reduce the effects of spectral variability and existence of anomalous pixels on endmember extraction results, an endmember extraction algorithm based on spatial pixel purity index was proposed, according to the different spectral characteristics of pure pixels and mixed pixels in local areas. The weighted addition of spectral angle distance and Euclidean distance was utilized as a new mixed distance metric, and then a fixed-size neighboring window was applied to compute the spatial pixel purity index of all the pixels in the image. On this basis, the endmemebers were searched sequentially based on the spectral angle distance metric and the predefined discrimination threshold of endmember spectra. Experimental results on both synthetic and real hyperspectral images demonstrate that the proposed algorithm can extract endmembers accurately, and it outperforms several other popular algorithms.

出版日期: 2013-09-01
:  TP 751  
基金资助:

国家自然科学基金资助项目(61171152);教育部科技支撑技术资助项目(625010216);浙江省自然科学基金资助项目(LY13F020044).

通讯作者: 厉小润,男,研究员.     E-mail: lxr@zju.edu.cn
作者简介: 崔建涛(1988- ),男,博士生,主要从事高光谱混合像元分解研究.E-mail:cuijiantao@zju.edu.cn
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引用本文:

崔建涛,王晶,厉小润,赵辽英. 基于空间像素纯度指数的端元提取算法[J]. J4, 2013, 47(9): 1517-1523.

CUI Jian-tao, WANG Jing,LI Xiao-run, ZHAO Liao-ying. Endmember extraction algorithm based on spatial pixel purity index. J4, 2013, 47(9): 1517-1523.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2013.09.002        http://www.zjujournals.com/eng/CN/Y2013/V47/I9/1517

[1] MIAO L D, QI H R. 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.
[2] BOARDMAN J W, KRUSE F A, GREEN R O. Mapping target signatures via partial unmixing of AVIRIS data: in Summaries [C]∥ In Fifth JPL Airborne Earth Science Workshop. Pasadena:\[s.n.\], 1995: 23-26.
[3] WINTER M E. N-FINDR: An algorithm for fast autonomous spectral end-member determination in hyperspectral data [C]∥ In SPIE Imaging Spectrometry V. San Diego CA:SPIE, 1999: 266-275.
[4] 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): 898-910.
[5] TAO X T, WANG B, ZHANG L M, et al. A new endmember extraction algorithm based on orthogonal bases of subspace formed by endmembers [C]∥ Geoscience and Remote Sensing Symposium. Barcelona, Spain:\[s.n.\], 2007: 2006-2009.
[6] Chang C I, WU C C, LIU W M, et al. A new growing method for simplex-based endmember extraction algorithm\[J\]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(10): 2804-2819.
[7] LUO W F, ZHONG L, ZHANG B. Null space spectral projection algorithm for hyperspectral image endmember extraction [J]. Journal of Infrared Milim Waves, 2010, 29(4): 307-311.
[8] DURAN O, PETROU M. Robust endmember extraction in the presence of anomalies [C]∥ IEEE International Geoscience and Remote Sensing Symposium. Cape Town:IEEE,2009, 4: 89-92.
[9] MEI S H, HE M Y, ZHANG Y F, et al. Improving spatial-spectral endmember extraction in the presence of anomalous ground objects [J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(11): 4210-4222.
[10] MARTIN G, PLAZA A. Region-based spatial preprocessing for endmember extraction and spectral unmixing [J]. IEEE Geoscience and Remote Sensing Letters, 2011, 8(4): 745-749.
[11] PLAZA A, MARTINEZ P, PEREZ R,et al. Spatial/spectral endmember extraction by multidimensional morphological operations [J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(9): 2025-2041.
[12] ROGGE D M, RIVARD B, ZHANG J, et al. Integration of spatial-spectral information for the improved extraction of endmembers [J]. Remote Sensing of Environment, 2007, 110(3): 287-303.
[13] MEI S H, HE M Y, WANG Z Y, et al. Spatial purity based endmember extraction for spectral mixture analysis [J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(9): 3434-3445.
[14] ZORTEA M, PLAZA A. Spatial preprocessing for endmember extraction [J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(8): 2679-2693.
[15] KESHAVA N. Distance metrics and band selection in hyperspectral processing with applications to material identification and spectral libraries[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(7): 1552-1564.
[16] DIAS J M B, NASCIMENTO J M P. Hyperspectral subspace identification [J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(8): 2435-2445.
[17] CHANG C I, DU Q. Estimation of number of spectrally distinct signal sources in hyperspectral imagery [J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(3): 608-619.
[18] 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]. Denver:USGS Spectroscopy Lab., 1993: 93592.
[19] GUO Z, WITTMAN T, OSHER S. L1 Unmixing and its application to hyperspectral image enhancement [C]∥ Proceedings of SPIE Conference on Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imager XV. Orlando:SPIE,2009: 73341M-73341M-9.
[20] LIU X S, XIA W, WANG B, et al. An approach based on constrained nonnegative matrix factorization to unmix hyperspectral data [J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(2): 757-772.

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