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
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.
林娜, 杨武年, 王斌. 基于核方法的高光谱遥感图像混合像元分解[J]. 国土资源遥感, 2017, 29(1): 14-20.
LIN Na, YANG Wunian, WANG Bin. Pixel un-mixing for hyperspectral remote sensing image based on kernel method. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(1): 14-20.
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2017, Vol. 29 
