Denoising of hyperspectral remote sensing images using NSCT and KPCA
结合NSCT和KPCA的高光谱遥感图像去噪

As hyperspectral remote sensing image is easily interfered by noises, a denoising method of hyperspectral remote sensing image based on Nonsubsampled Contourlet Transform (NSCT) and Kernel Principal Component Analysis (KPCA) is proposed. First, hyperspectral image of each band is decomposed by NSCT to acquire the coefficients which are processed by KPCA. The proper principal components are selected for KPCA reconstruction according to noise features. Finally, the denoised image is obtained by performing inverse NSCT. Experimental results show that the proposed method can suppress noise interference in hyperspectral remote sensing images, and preserve the useful information of original data more completely.