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 interfer- ence in hyperspectral remote sensing images

and preserve the useful information of original data more completely.