改进的空间信息约束非负矩阵分解的高光谱图像解混算法
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摘要
传统的高光谱混合像元分解方法仅考虑高光谱图像的几何特性或者丰度的稀疏性,而忽略高光谱数据的光谱空间特性。当原图像中不存在纯净像元时,分解精度将严重下降。为了解决这些问题,提出一种改进的空间信息约束非负矩阵分解的解混算法,该方法充分利用高光谱图像的空间信息和稀疏性,提高了传统非负矩阵分解算法的性能。合成的模拟图像和真实的高光谱图像实验表明,该方法克服了传统方法对噪声的敏感性及对纯像元的依赖性。
The traditional hyperspectral unmixing methods only consider the geological properties of hyperspectral images or the sparse properties of abundance and neglect the spectral spatial information of hyperspectral data.Thus when the pure pixels are missing,the unmixing accuracy is significantly reduced.In order to overcome these limitations,an improved spatial information constrained nonnegative matrix factorization method for unmixing is proposed.This method fully uses the spatial information and the sparse properties of hyperspectral images,and thus the properties of the traditional nonnegative matrix factorization methods are improved.Both the synthetic simulation images and the experimental results show that the proposed method has overcome the noise-sensitivity and the dependence on pure pixels of the traditional methods.
The traditional hyperspectral unmixing methods only consider the geological properties of hyperspectral images or the sparse properties of abundance and neglect the spectral spatial information of hyperspectral data.Thus when the pure pixels are missing,the unmixing accuracy is significantly reduced.In order to overcome these limitations,an improved spatial information constrained nonnegative matrix factorization method for unmixing is proposed.This method fully uses the spatial information and the sparse properties of hyperspectral images,and thus the properties of the traditional nonnegative matrix factorization methods are improved.Both the synthetic simulation images and the experimental results show that the proposed method has overcome the noise-sensitivity and the dependence on pure pixels of the traditional methods.
引文
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[18]Qian Y T,Jia S,Zhou J,et al.Hyperspectral unmixing via L1/2 sparsity constrained nonnegative matrix factorization[J].IEEE Transactions on Geoscience and Remote Sensing,2011,49(11):4282-4297.
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[20]M,Niyogi P.Laplacian eigenmaps and spectral techniques for embedding and clustering[C]∥Advances in Neural Information Processing systems,December 03-08,Vancouver,British Columbia,Canada.USA:MIT Press,2002:585-591.
[21]Wang Q,Lin J Z,Yuan Y.Salient band selection for hyperspectral image classification via manifold ranking[J].IEEE Transactions on Neural Networks and Learning Systems,2016,27(6):1279-1289.
[22]Lee D D,Seung H S.Learning the parts of objects by non-negative matrix factorization[J].Nature,1999,401(6755):788-791.
[23]LüY L.Hyperspectral unmixing theory and techniques based on nonnegative matrix factorization[D].Hangzhou:Hangzhou Dianzi University,2009:32-33.吕亚丽.基于非负矩阵分解的高光谱图像解混技术研究[D].杭州:杭州电子科技大学,2009:32-33.
[2]Liao J S,Wang L G.Hyperspectral image classification method based on fusion with two kinds of spatial information[J].Laser&Optoelectronics Progress,2017,54(8):081002.廖建尚,王立国.两类空间信息融合的高光谱图像分类方法[J].激光与光电子学进展,2017,54(8):081002
[3]Dong A G,Li J X,Zhang B,et al.Hyperspectral image classification algorithm based on spectral clustering and sparse representation[J].Acta Optica Sinica,2017,37(8):0828005.董安国,李佳逊,张蓓,等.基于谱聚类和稀疏表示的高光谱图像分类算法[J].光学学报,2017,37(8):0828005.
[4]Zhang B,Gao L R.Hyperspectral image classification and target detection[M].Beijing:Science Press,2011:15-23.张兵,高连如.高光谱图像分类与目标探测[M].北京:科学出版社,2011:15-23.
[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):898-910.
[6]Winter M E.N-FINDR:an algorithm for fast autonomous spectral end-member determination in hyperspectral data[J].Proceedings of SPIE,1999,3753:266-275.
[7]Boardman J W,Kruse F A,Green R O.Mapping target signatures via partial unmixing of AVIRIS data[C]∥Proceedings of Summaries of the Fifth Annual JPL Airborne Earth Science Workshop,January 23-26,1995,Pasadena,USA.Pasadena:AVIRISWorkshop,1995:23-26.
[8]Bioucas-Dias J M.A variable splitting augmented Lagrangian approach to linear spectral unmixing[C]∥2009 First Workshop on Hyperspectral Image and Signal Processing:Evolution in Remote Sensing,August 26-28,2009,Grenoble,France.New York:IEEE,2009:5289072.
[9]Heinz D C,Chein-I-Chang.Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery[J].IEEE Transactions on Geoscience and Remote Sensing,2001,39(3):529-545.
[10]Wang J,Chang C I.Applications of independent component analysis in endmember extraction and abundance quantification for hyperspectral imagery[J].IEEE Transactions on Geoscience and Remote Sensing,2006,44(9):2601-2616.
[11]Miao L D,Qi H R.Endmember extraction from highly mixed data using minimum volume constrained nonnegative matrix factorization[J].IEEETransactions on Geoscience and Remote Sensing,2007,45(3):765-777.
[12]Wang N,Du B,Zhang L P.An endmember dissimilarity constrained non-negative matrix factorization method for hyperspectral unmixing[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2013,6(2):554-569.
[13]Rajabi R,Ghassemian H.Spectral unmixing of hyperspectral imagery using multilayer NMF[J].IEEE Geoscience and Remote Sensing Letters,2015,12(1):38-42.
[14]Li D G,Li S T,Li H L.Hyperspectral image unmixing based on sparse and minimum volume constrained nonnegative matrix factorization[C]∥Proceedings of 6th Chinese Conference on Pattern Recognition,November 17-19,Changsha,China.Heidelberg:Springer,2014:44-52.
[15]Yu Y,Guo S,Sun W D.Minimum distance constrained nonnegative matrix factorization for the endmember extraction of hyperspectral images[J].Proceedings of SPIE,2007,6790:151-159.
[16]Wu C Y,Shen C M.Spectral unmixing using sparse and smooth nonnegative matrix factorization[C]∥2013 21st International Conference on Geoinformatics,June 20-22,2013,Kaifeng,China.New York:IEEE,2013:6626115.
[17]Yang Z Y,Zhou G X,Xie S L,et al.Blind spectral unmixing based on sparse nonnegative matrix factorization[J].IEEE Transactions on Image Processing,2011,20(4):1112-1125.
[18]Qian Y T,Jia S,Zhou J,et al.Hyperspectral unmixing via L1/2 sparsity constrained nonnegative matrix factorization[J].IEEE Transactions on Geoscience and Remote Sensing,2011,49(11):4282-4297.
[19]Lu X Q,Wu H,Yuan Y,et al.Manifold regularized sparse NMF for hyperspectral unmixing[J].IEEETransactions on Geoscience and Remote Sensing,2013,51(5):2815-2826.
[20]M,Niyogi P.Laplacian eigenmaps and spectral techniques for embedding and clustering[C]∥Advances in Neural Information Processing systems,December 03-08,Vancouver,British Columbia,Canada.USA:MIT Press,2002:585-591.
[21]Wang Q,Lin J Z,Yuan Y.Salient band selection for hyperspectral image classification via manifold ranking[J].IEEE Transactions on Neural Networks and Learning Systems,2016,27(6):1279-1289.
[22]Lee D D,Seung H S.Learning the parts of objects by non-negative matrix factorization[J].Nature,1999,401(6755):788-791.
[23]LüY L.Hyperspectral unmixing theory and techniques based on nonnegative matrix factorization[D].Hangzhou:Hangzhou Dianzi University,2009:32-33.吕亚丽.基于非负矩阵分解的高光谱图像解混技术研究[D].杭州:杭州电子科技大学,2009:32-33.