Abstract：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.
常威威; 郭 雷; 刘 坤; 付朝阳. 基于Contourlet变换和主成分分析的高光谱数据噪声消除方法[J]. 电子与信息学报, 2009, 31(12): 2892-2896 .
Chang Wei-wei; Guo Lei; Liu Kun; Fu Zhao-yang. Denoising of Hyperspectral Data Based on Contourlet Transform and Principal Component Analysis. , 2009, 31(12): 2892-2896 .
2009, Vol. 31 
