一种考虑光谱变异性的高光谱图像非线性解混算法
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摘要
非线性解混可以解释高光谱图像复杂场景中的非线性混合效应,但地物的光谱变异性是其中的一个难点。提出一种考虑光谱变异性的无监督非线性解混算法。通过核函数将原始高光谱图像数据隐式地映射到高维特征空间中,从而在该空间中结合光谱变异性进行线性解混;与此同时,依据实际地物的分布特性,添加丰度和光谱变异系数的局部平滑约束。模拟和真实高光谱数据的实验结果表明,该方法能克服不同非线性混合场景中存在的光谱变异性问题,提高光谱解混的精度。
Nonlinear unmixing can explain the nonlinear mixing effect in complex scenarios of hyperspectral imagery,but the spectral variability of ground objects is one of the difficulties. An unsupervised nonlinear unmixing algorithm dealing with spectral variability is proposed in this paper. The original hyperspectral image data is implicitly mapped into a high-dimensional feature space through a kernel function and then linear unmixing is applied for hyperspectral imagery in combination with spectral variability in this space. Further,local smoothness constraint is added on abundances and coefficients of spectral variability according to the distribution characteristics of ground objects. Experimental results on simulated and real hyperspectral data indicate that the proposed algorithm can overcome the spectral variability problem in different nonlinear mixing scenarios and improve the unmixing accuracy.
Nonlinear unmixing can explain the nonlinear mixing effect in complex scenarios of hyperspectral imagery,but the spectral variability of ground objects is one of the difficulties. An unsupervised nonlinear unmixing algorithm dealing with spectral variability is proposed in this paper. The original hyperspectral image data is implicitly mapped into a high-dimensional feature space through a kernel function and then linear unmixing is applied for hyperspectral imagery in combination with spectral variability in this space. Further,local smoothness constraint is added on abundances and coefficients of spectral variability according to the distribution characteristics of ground objects. Experimental results on simulated and real hyperspectral data indicate that the proposed algorithm can overcome the spectral variability problem in different nonlinear mixing scenarios and improve the unmixing accuracy.
引文
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[2]Keshava N,Mustard J F.Spectral unmixing[J].IEEESignal Process.Mag.,2002,19(1):44-57.
[3]Bioucas-Dias J M,Plaza A,Dobigeon N,et al.Hyperspectral unmixing overview:Geometrical,statistical,and sparse regression-based approaches[J].IEEE J.Sel.Topics Appl.Earth Observ.Remote Sens.,2012,5(2):354-379.
[4]Somers B,Asner G P,Tits L,et al.Endmember variability in spectral mixture analysis:A review[J].Remote Sensing of Environment,2011,115(7):1603-1616.
[5]Thouvenin P A,Dobigeon N,Tourneret J Y.Hyperspectral unmixing with spectral variability using a perturbed linear mixing model[J].IEEE Trans.Image Process.,2016,64(2):525-538.上接第页
[6]Drumetz L,Veganzones M A,Henrot S,et al.Blind hyperspectral unmixing using an Extended Linear Mixing Model to address spectral variability[J].IEEE Trans.Image Process.,2016,25(8):3890-3905.
[7]Heylen R,Parente M,Gader P.A review of nonlinear hyperspectral unmixing methods[J].IEEE J.Sel.Topics Appl.Earth Observ.Remote Sens.,2014,7(6):1844-1868.
[8]YANG Bin,WANG Bin.Review of nonlinear unmixing for hyperspectral remote sensing imagery[J].J.Infrared Millim.Waves.(杨斌,王斌.高光谱遥感图像非线性解混综述.红外与毫米波学报),2017,36(2):173-185.
[9]Hapke B W.Bidirectional reflectance spectroscopy.I.Theory[J].J.Geophys.Res.,1981,86:3039-3054.
[10]Halimi A,Altmann Y,Dobigeon N,et al.Nonlinear unmixing of hyperspectral images using a generalized bilinear model[J].IEEE Trans.Geosci.Remote Sens.,2011,49(11):4153-4162.
[11]Ammanouil R,Ferrari A,Richard C,et al.Nonlinear unmixing of hyperspectral data with vector-valued kernel functions[J].IEEE Trans.Image Process.,2017,26(1):340-354.
[12]Chen J,Richard C,Honeine P.Nonlinear unmixing of hyperspectral images based on multi-kernel learning[C].Hyperspectral Image and Signal Processing(WHISPERS),2012 4th Workshop on.IEEE,2012.
[13]ZHU Fei,Honeine P.Biobjective Nonnegative Matrix Factorization:Linear Versus Kernel-Based Models[J].IEEETrans.Geosci.Remote Sens.,2016,54(7):4012-4022.
[14]Smola A J,Sch?lkopf B.Learning with kernels[C].Cambridge:MIT Press,2008.
[15]Camps-Valls G,Bruzzone L.Kernel-based methods for hyperspectral image classification[J].IEEE Trans.Geosci.Remote Sens.,2005,43(6):1351-1362.
[16]Evgeniou T,Micchelli C A,Pontil M.Learning multiple tasks with kernel methods[M].J.Mach.Learn.Res.,2005,6:615-637.
[17]TANG Yi,WAN Jian-Wei,XU Ke,et al.Hyperspectral unmixing based on material spatial distribution characteristic[J].J.Infrared Millim.Waves.(汤毅,万建伟,许可等.地物空间分布特性的高光谱遥感图像解混算法.红外与毫米波学报),2014,33(5):560-570.
[18]Lin C J.Projected gradient methods for nonnegative matrix factorization[J].Neural computation,2007,19(10):2756-2779.
[19]Huck A,Guillaume M,Blanc-Talon J.Minimum dispersion constrained nonnegative matrix factorization to unmix hyperspectral data[J].IEEE Trans.Geosci.Remote Sens.,2010,48(6):2590-2602.
[20]Boyd S,Parikh N,Chu E,et al.Distributed optimization and statistical learning via the alternating direction method of multipliers[J].Foundations and Trendsin Machine Learning,2011,3(1):1-122.
[21]Alfonso M V,Dias J M B,Figueiredo M A T.Fast image recovery using variable splitting and constrained optimization[J].IEEE Trans.Image Process.,2010,19(9):2345-2356.
[22]Nascimento J M P,Dias J M B.Vertex component analysis:A fast algorithm to unmix hyperspectral data[J].IEEE Trans.Geosci.Remote Sens.,2005,43(4):898-910.
[23]Heinz D C,Chang C.Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery[J].IEEE Trans.Geosci.Remote Sens.,2001,39(3):529-545.
[24]QIAN Yun-Tao,JIA Sen,ZHOU Jun,et al.Hyperspectral unmixing via L1/2sparsity-constrained nonnegative matrix factorization[J].IEEE Trans.Geosci.Remote Sens.,2011,49(11):4282-4297.
[25]LI Xiao-Rui,CUI Jian-Tao,ZHAO Liao-Ying.Blind nonlinear hyperspectral unmixing based on constrained kernel nonnegative matrix factorization[J].Signal,Image and Video Processing,2014,8(8):1555-1567.
[26]Swayze G,Clark R,Sutley S,et al.Ground-truthing AVIRIS mineral mapping at Cuprite,Nevada[J].Summaries of the Third Annual JPL Airborne Geosciences Workshop,1992,AVIRIS Workshop JPL Publication:47-49.