Research of hyperspectral unmixing methods
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摘要: 高光谱图像的空间分辨率较低，导致大量混合像元存在于高光谱图像中。混合像元的存在是使高光谱图像目标分类准确率降低的主要原因之一。高光谱像元解混在高光谱遥感图像处理中具有非常重要的意义。高光谱像元解混主要分为线性和非线性光谱解混两种方法，研究最广泛的是线性光谱解混。归纳了线性光谱解混的两个步骤：（1）提取纯净像元中地物的光谱信号，即提取端元，这是关键步骤；（2）利用端元的加权线性组合对混合像元进行光谱解混，即丰度反演。简述了端元提取及丰度反演研究的主要进展，介绍了端元提取的几种典型算法。通过归纳、对比和分析，总结了不同端元提取方法的特点，并对高光谱解混的研究前景进行了展望。Abstract: Because spatial resolution of hyper-spectral images is low, a large number of the mixed pixels were in hyper-spectral images. The presence of the mixed pixels is one of the main reasons of the low accuracy of target classification in hyper-spectral images. Hyper spectral pixel unmixing is of great importance in hyper-spectral remote sensing image processing. Hyper-spectral unmixing is divided into two methods:linear and nonlinear spectral unmixing. Linear spectral unmixing has been studied most widely. Two steps of linear spectral mixing are summed up:firstly, spectral signals of ground objects in the pure pixels are extracted, that is, end-members are extracted. It is the key step. The weighted linear combination of end-members is used to unmix the spectral image of the mixed pixels, that is, the abundance inversion. Main progress of end-member extraction and abundances inversion is briefly introduced, and several typical algorithms for end-member extraction are introduced. Through summing-up, contrasting and analyzing, the characteristics of different endmember extraction methods are summarized. The prospect of hyperspectral unmixing is prospected.
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Keywords:
- spectroscopy /
- hyperspectral image /
- linear unmixing /
- endmember extraction
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表 1 端元提取算法的均方根误差
方法 PPI N-FINDR ATGP SMACC IEA 均方根误差 231.6982 138.7092 398.2158 335.0004 106.1878 
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表 2 常用端元提取算法的特点
方法 全自动 降维 使用空间信息 算法复杂性 解混精度 PPI 否 是 否 容易 较高 N-FINDR 是 是/否 否 容易 高 ATGP 是 否 否 容易 较低 SMACC 是 否 否 容易 较低 NMF 是 否 否 复杂 较高 IEA 是 否 否 复杂 高 AMEE 是 否 是 容易 高
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[1] ZHANG L P, ZHANG L F. Hyperspectral remote sensing[M].Wuhan:Wuhan University Press, 2005:26-27(in Chinese).
[2] XIA W. Researches on the methods of unmixing and band selection for hyperspectral remote sensing images[D]. Shanghai: Fudan University, 2013: 15-16(in Chinese).
[3] NASH D B, CONEL J E. Spectral reflectance systematics for mixtures of powdered hypersthene, labradorite, and ilmenite[J].Journal of Geophysical Research, 1974, 79(11):1615-1621. DOI: 10.1029/JB079i011p01615
[4] BRUCE H. Book review:Theory of reflectance and emittance spectroscopy[M].Cambridge:Cambridge University Press, 1993:455-460.
[5] BOARDMAN J W, KRUSE F A, GREEN R O.Mapping target signatures via partial unmixing of AVIRIS data in summaries[C]//Summaries, Fifth JPL Airborne Earth Science Workshop. Pasadena, USA: JPL Publication, 1995: 23-262.
[6] WINTER M E. N-FINDR:an algorithm for fast autonomous spectral endmember determination in hyperspectral data[J].Proceedings of the SPIE, 1999, 3753:266-275. DOI: 10.1117/12.366289
[7] NASCIMENTO J M P, DIAS J M B.Vertex component analysis:a fast algorithm to unmix hyperspectral data[J].IEEE Transactions on Geosciences and Remote Sensing, 2005, 43(4):898-910. DOI: 10.1109/TGRS.2005.844293
[8] ZHUO L, CAO J J, WANG F, et al. Blind unmixing based on improved target endmember for hyperspectral imagery[J]. Journal of Remote Sensing, 2015, 19(2):273-287(in Chinese). http://d.old.wanfangdata.com.cn/Periodical/ygxb201502010
[9] HARSANYI J C, CHANG C I. Hyperspectral image classification and dimensionality reduction:an orthogonal subspace projection approach[J].IEEE Transactions on Geoscience and Remote Sensing, 1994, 32(4):779-785. DOI: 10.1109/36.298007
[10] GRUNINGER J, RATKOWSKI A J, HOKE M L. The sequential maximum angle convex cone endmember model[J].Proceedings of the SPIE, 2004, 5425:1-14. DOI: 10.1117/12.543794
[11] JIA S, QIAN Y T. Constrained nonnegative matrix factorization for hyperspectral unmixing[J].IEEE Transaction on Geoscience and Remote Sensing, 2009, 47(1):161-173. DOI: 10.1109/TGRS.2008.2002882
[12] HYVARINEN A, KARHUNEN J, OJA E. Independent component analysis[M].New York, USA:John Wiley & Sons Ltd, 2001:89-96.
[13] ZHAO Ch H, CUI Sh L, ZHAO G P, et al. Endmember extraction algorithm based on improved iterative error analysis[J]. Journal of Harbin Engineering University, 2015, 36(2):257-261(in Chinese).
[14] PLAZA A, MARTINEZ P, PEREZ R, et al. Spatial/spectral endmember extraction by multidimensional morphological operations[J].IEEE Transactions on Geosciences and Remote Sensing, 2002, 40(9):2025-2040. DOI: 10.1109/TGRS.2002.802494
[15] SHEN W X. Simplex theory guidance:high dimensional generalization of triangles[M].Changsha:Hunan Normal University Press, 2000:35(in Chinese).
[16] ANTONIO P, PABLO M, ROSA P, et al. A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data[J].IEEE Transaction on Geoscience and Remote Sensing, 2004, 42(3):650-663. DOI: 10.1109/TGRS.2003.820314
[17] WANG K, QU H M. Anomaly detection method based on improved minimum noise fraction transformation[J].Laser Technology, 2015, 39(3):381-385(in Chinese). http://www.en.cnki.com.cn/Article_en/CJFDTOTAL-JGJS201503022.htm
[18] ZENG F X. The improvement and optimization of endmembers extraction in hyperspectral remote sensing image[D].Chengdu: Chengdu University of Technology, 2013: 19-20(in Chinese).
[19] GENG X, ZHAO Y, WANG F, et al. A new volume formula for a simplex and its application to endmember extraction for hyperspectral image analysis[J].International Journal of Remote Sensing, 2010, 31(4):1027-1035. DOI: 10.1080/01431160903154283
[20] CHARLES L, LAWSON, RICHARD J. Solving least squares pro-blem[M].New Jersey, USA:Prentice-Hall, 1995:64-80.
[21] HEINZ D C, CHANG C I. Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery[J].IEEE Transaction on Geoscience and Remote Sensing, 2001, 39(3):529-545. DOI: 10.1109/36.911111
[22] MEI Sh H, HE M Y. A novel spectrum filter for fully constrained mixture analysis[J]. Journal of Remote Sensing, 2010, 14(1):68-79. http://en.cnki.com.cn/Article_en/CJFDTotal-YGXB201001008.htm
[23] LEE D D, SEUNG H S. Learning the parts of objects by nonnegative matrix factorization[J].Nature, 1999, 401(6755):788-791. DOI: 10.1038/44565
[24] MIAO L, QI H. Endmember extraction from highly mixed data using minimum volume constrained nonnegative matrix fraction[J].IEEE Transaction on Geoscience and Remote Sensing, 2007, 45(3):765-777. DOI: 10.1109/TGRS.2006.888466
[25] QIAN Y T, JIA S. Hyperspectral unmixing via L1/2 sparsity-constrained nonnegative matrix factorization[J].IEEE Transaction on Geoscience and Remote Sensing, 2011, 49(11):4282-4297. DOI: 10.1109/TGRS.2011.2144605
[26] YU Y, GUO Sh, SUN W D. Minimum distance constrained nonnegative matrix factorization for the end-member extraction of hyperspectral images[J]. High Technology Letters, 2012, 18(4):333-342. http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=828882
[27] BAYLISS J D, GUALTIERI J A, CROMP R F. Analyzing hyperspectral data with independent component analysis[J].Proceedings of the SPIE, 1998, 3240:133-143. DOI: 10.1117/12.300050
[28] CHEN C H, ZHANG X. Independent component analysis for remote sensing study[J].Proceedings of the SPIE, 1999, 3871:150-158. DOI: 10.1117/12.373252
[29] CHIANG S S, CHANG C I, GINSBERG I W. Unsupervised hyperspectral image analysis using independent component analysis[C]//Proceedings, Geoscience and Remote Sensing Symposium. Honolulu, HI, USA: IEEE International Geoscience & Remote Sensing, 2000: 3136-3138.
[30] ZORTEA M, PLAZA A. Spatial preprocessing for endmember extraction[J]. IEEE Transaction on Geoscience and Remote Sensing, 2009, 47(8):2679-2693. DOI: 10.1109/TGRS.2009.2014945
[31] MARTIN G, PLAZA A. Region-based spatial preprocessing for endmember extraction and spectral unmixing[J].IEEE Transaction on Geoscience and Remote Sensing, 2011, 8(4):745-749. DOI: 10.1109/LGRS.2011.2107877
[32] MARTIN G, PLAZA A. Spatial-spectral preprocessing prior to endmember identification and unmixing of remotely sensed hyperspectral data[J].IEEE Transaction on Geoscience and Remote Sensing, 2012, 5(2):380-395. http://ieeexplore.ieee.org/document/6179313/
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