利用核方法进行高光谱遥感图像线性解混

为了提高高光谱遥感图像混合像元分解的精度，提出基于核方法的高光谱线性解混。采用核化正交子空间投影（orthogonal subspace projection，OSP）算子、最小二乘正交子空间投影（least squares OSP，LSOSP）算子、非负约束最小二乘（nonnegative constrained least-squares，NCLS）算子和全约束最小二乘（fully constrained least-squares，FCLS）算子等方法分别构建核正交子空间投影（Kernel OSP，KOSP）、核最小二乘正交子空间投影（Kernel LSOSP，KLSOSP）、核非负约束最小二乘（Kernel NCLS，KNCLS）和核全约束最小二乘（Kernel FCLS，KFCLS）高光谱图像混合像元解混模型。对CUPRITE矿区AVIRIS数据进行KLSOSP、KNCLS和KFCLS与LSOSP、NCLS和FCLS丰度反演对比实验，结果表明，对于混合像元广泛存在的高光谱遥感图像来说，基于核方法的KLSOSP，KNCLS和KFCLS的解混精度优于LSOSP，NCLS和FCLS；附加约束条件有利于提高丰度反演的精度。

Using Kernel Method to Linearly Un-mixing Hyperspectral Pixel

In order to improve the accuracy of hyperspectral pixel un-mixing, a Kernel based pixel un-mixing method was proposed in this paper. By kernelizing 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) to hyperspectral imagery pixel un-mixing. The comparison experiments of abundance inversion by using KLSOSP, KNCLS, KFCLS and LSOSP, NCLS, FCLS to CUPRITE AVIRIS data were carried out. 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.