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REMOTE SENSING FOR LAND & RESOURCES    2017, Vol. 29 Issue (1) : 14-20     DOI: 10.6046/gtzyyg.2017.01.03
Technology and Methodology |
Pixel un-mixing for hyperspectral remote sensing image based on kernel method
LIN Na1,2, YANG Wunian2, WANG Bin3
1. School of Civil Engineering, Chongqing Jiaotong University, Chongqing 400074, China;
2. Key Laboratory of Geoscience Spatial Information Technology, Ministry of Land and Resources, Chengdu University of Technology, Chengdu 610059, China;
3. Chongqing Geomatics Center, Chongqing 401121, China
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In order to improve the accuracy of hyperspectral pixel un-mixing,the authors proposed a kernel based pixel un-mixing method in this paper. By adopting orthogonal subspace projection(OSP) operator, least squares OSP(LSOSP) operator, nonnegative constrained least squares(NCLS) operator and fully constrained least squares(FCLS) operator respectively, the authors established kernel OSP(KOSP),kernel LSOSP(KLSOSP),kernel NCLS(KNCLS) and kernel FCLS(KFCLS) for hyperspectral imagery pixel un-mixing. The comparative experiments of abundance inversion by applying KLSOSP, KNCLS, KFCLS and LSOSP, NCLS, FCLS to CUPRITE AVIRIS data were carried out,and the results show that, for heavily mixed hyperspectral images, the pixel un-mixing accuracy of kernels based KLSOSP,KNCLS and KFCLS is higher than that of LSOSP, NCLS and FCLS. Meanwhile,the constraint conditions can improve the accuracy of abundance estimates.

Keywords GF-1      ZY-3      rational function model      bundle adjustment      joint satellite geo-positioning     
:  TP751.1  
Issue Date: 23 January 2017
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XIE Yong
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LIU Qiyue
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Cite this article:   
HAN Jie,XIE Yong,WU Guoxi, et al. Pixel un-mixing for hyperspectral remote sensing image based on kernel method[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(1): 14-20.
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[1] 李德仁,童庆禧,李荣兴,等.高分辨率对地观测的若干前沿科学问题[J].中国科学:地球科学,2012,42(6):805-813. Li D R,Tong Q X,Li R X,et al.Current issues in high-resolution Earth observation technology[J].Science China Earth Sciences,2012,55(7):1043-4051.
[2] Chang C I.Hyperspectral Data Processing:Algorithm Design and Analysis[M].Hoboken,NJ,USA:John Wiley and Sons Inc,2013.
[3] Zhang L P,Huang X.Advanced processing techniques for remotely sensed imagery[J].Journal of Remote Sensing,2009,13(4):559-569.
[4] Rosin P L.Robust pixel unmixing[J].IEEE Transactions on Geoscience and Remote Sensing,2001,39(9):1978-1983.
[5] 普晗晔,王斌,张立明.基于单形体几何的高光谱遥感图像解混算法[J].中国科学:信息科学,2012,42(8):1019-1033. Pu H Y,Wang B,Zhang L M.Simplex geometry-based abundance estimation algorithm for hyperspectral unmixing[J].Scientia Sinica Informationis,2012,42(8):1019-1033.
[6] Wirasakti S,Zein R A,Mafazi F.Comparative study of land cover linear spectral mixture analysis(LSMA)model on multispectral and hyperspectral imagery[C]//34th Asian Conference on Remote Sensing.Bali,Indonesia:[s.n.],2013.
[7] 赵春晖,肖健钰.一种利用互信息加权的最小二乘法丰度反演算法[J].沈阳大学学报:自然科学版,2014,26(1):45-49. Zhao C H,Xiao J Y.An abundance inversion algorithm based on mutual information weighted least square error[J].Journal of Shenyang University:Natural Science,2014,26(1):45-49.
[8] 唐晓燕,高昆,倪国强.高光谱图像非线性解混方法的研究进展[J].遥感技术与应用,2013,28(4):731-738. Tang X Y,Gao K,Ni G Q.Nonlinear spectral unmixing of hyperspectral images[J].Remote Sensing Technology and Application,2013,28(4):731-738.
[9] Camps-Valls G,Bruzzone L.Kernel Methods for Remote Sensing Data Analysis[M].Chichester:John Wiley and Sons Ltd,2009.
[10] Hosseini S A,Ghassemian H.A new fast algorithm for multiclass hyperspectral image classification with SVM[J].International Journal of Remote Sensing,2011,32(23):8657-8683.
[11] Broadwater J,Banerjee A.Mapping intimate mixtures using an adaptive kernel-based technique[C]//Proceeding of the 3rd Workshop on Hyperspectral Image and Signal Processing:Evolution in Remote Sensing.Lisbon:IEEE,2011:1-4.
[12] 刘婷婷,林珲,张良培,等.利用SVM相关反馈和语义挖掘的遥感影像检索[J].武汉大学学报:信息科学版,2012,37(4):415-418. Liu T T,Lin H,Zhang L P,et al.SVM-relevance-feedback and semantic-extraction-based RS image retrieval[J].Geomatics and Information Science of Wuhan University,2012,37(4):415-418.
[13] 王晓飞,张钧萍,张晔.高光谱图像混合像元分解算法[J].红外与毫米波学报,2010,29(3):210-215,229. Wang X F,Zhang J P,Zhang Y.Unmixing algorithm of hyperspectral images[J].Journal of Infrared and Millimeter Waves,2010,29(3):210-215,229.
[14] 谭熊,余旭初,张鹏强,等.基于多核支持向量机的高光谱影像非线性混合像元分解[J].光学精密工程, 2014,22(7):1912-1920. Tan X,Yu X C,Zhang P Q,et al.Nonlinear mixed pixel decomposition of hyperspectral imagery based on multiple kernel SVM[J].Optics and Precision Engineering,2014,22(7):1912-1920.
[15] Kwon H,Nasrabadi N M.Kernel orthogonal subspace projection for hyperspectral signal classification[J].IEEE Transactions on Geoscience and Remote Sensing,2005,43(12):2952-2962.
[16] Liu K H,Wong E,Du E Y,et al.Kernel-based linear spectral mixture analysis[J].IEEE Geoscience and Remote Sensing Letters,2012,9(1):129-133.
[17] 王挺,杜博,张良培.顾及局域信息的核化正交子空间投影目标探测方法[J].武汉大学学报:信息科学版,2013,38(2):200-203,239. Wang T,Du B,Zhang L P.A local information-based kernelized OSP method for target detection[J].Geomatics and Information Science of Wuhan University,2013,38(2):200-203,239.
[18] 赵春晖,尤佳,李晓慧.基于自适应核方法的正交子空间投影异常检测算法[J].黑龙江大学自然科学学报,2012,29(2):254-258,272. Zhao C H,You J,Li X H.An orthogonal subspace projection anomaly detection algorithm based on adaptive kernel method[J].Journal of Natural Science of Heilongjiang University,2012,29(2):254-258,272.
[19] Bajorski P.Analytical comparison of the matched filter and orthogonal subspace projection detectors for hyperspectral images[J].IEEE Transactions on Geoscience and Remote Sensing,2007,45(7):2394-2402.
[20] Capobianco L,Garzelli A,Camps-Valls G.Target detection with semisupervised kernel orthogonal subspace projection[J].IEEE Transactions on Geoscience and Remote Sensing,2009,47(11):3822-3833.
[21] Fauvel M,Chanussot J,Benediktsson J A.Kernel principal component analysis for the classification of hyperspectral remote sensing data over urban areas[J].EURASIP Journal on Advances in Signal Processing,2009,2009:783194.
[22] 林娜,杨武年,王斌.高光谱遥感影像核最小噪声分离变换特征提取[J].武汉大学学报:信息科学版,2013,38(8):988-992. Lin N,Yang W N,Wang B.Hyperspectral image feature extraction via kernel minimum noise fraction transform[J].Geomatics and Information Science of Wuhan University,2013,38(8):988-992.
[23] Molero J M,Garzón E M,García I,et al.Anomaly detection based on a parallel kernel RX algorithm for multicore platforms[J].Journal of Applied Remote Sensing,2012,6(1):061503.
[24] Broadwater J,Chellappa R,Banerjee A,et al.Kernel fully constrained least squares abundance estimates[C]//Proceedings of IEEE International Geoscience and Remote Sensing Symposium.Barcelona,Spain:IEEE,2007:4041-4044.
[25] 林娜,杨武年,王斌.基于KMNF和BP神经网络的高光谱遥感影像分类[J].计算机工程与设计,2013,34(8):2774-2777,2782. Lin N,Yang W N,Wang B.Hyperspectral image classification on KMNF and BP neural network[J].Computer Engineering and Design,2013,34(8):2774-2777,2782.
[26] 林娜,杨武年,王斌.基于核最小噪声分离变换的高光谱遥感影像多类SVM分类[J].计算机应用与软件,2014,31(6):116-119. Lin N,Yang W N,Wang B.Multi-class SVM classification for hyperspectral remote sensing image based on kernel minimum noise fraction transform[J].Computer Applications and Software,2014,31(6):116-119.
[27] Swayze G A,Clark R N,Goetz A F H,et al.Mapping advanced argillic alteration at Cuprite,Nevada,using imaging spectroscopy[J].Economic Geology,2014,109(5):1179-1221.
[28] 林娜,杨武年,王斌.基于FLAASH的AVIRIS高光谱影像大气校正[J].地理空间信息,2013,11(4):49-50,54. Lin N,Yang W N,Wang B.Atmospheric correction of AVIRIS hyperspectral image based on FLAASH[J].Geospatial Information,2013,11(4):49-50,54.

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