针对高光谱遥感图像易受噪声干扰,本文提出了一种基于非下采样Contourlet变换NSCT(Nonsubsampled Contourlet Transform)和核主成分分析KPCA(Kernel Principal Component Analysis)的去噪方法。首先对高光谱各波段图像进行NSCT分解;然后利用KPCA对NSCT系数进行处理,并在KPCA重构时依据各类噪声的特性选取合适的主成分;最后用处理过的系数进行逆变换得到去噪图像。实验结果表明,本文方法抑制了高光谱遥感图像中的噪声干扰,较完整地保留了原始数据的有效信息。 Aiming at the noise-susceptibility of hyperspectral remote sensing images, a denoising method based on Nonsubsampled Contourlet Transform (NSCT) and Kernel Principal Component Analysis (KPCA) is proposed in this paper. Firstly, NSCT decomposition is performed on each band of the hyperspectral image. Then, the NSCT coefficients are processed by KPCA, and the appropriate principal components are selected according to the characteristics of various types of noise during KPCA reconstruction. Finally, the denoised image. Experimental results show that the proposed method suppresses the noise in hyperspectral remote sensing image and retains the complete information of the original data more completely.