Denoising of hyperspectral data based on contourlet transform and principal component analysis

This paper proposes a denoising method of hyperspectral super-dimensional data based on Contourlet transform and principal component analysis. At first the sparse representation of images is accomplished with Contourlet transform. Then the Contourlet coefficients are processed with principal component analysis. The experimental results based on OMIS images show that the proposed method can simultaneously eliminate noises in multi-band hyperspectral images, improve the quality of the whole hyperspectral data and outperforms methods based on PCA and Contourlet transform respectively.