CAJ | 학술논문

该文提出了一种适合于高光谱超维数据处理的基于Contourlet变换和主成分分析的噪声消除方法.该方法首先利用Contourlet变换实现图像的稀疏表示,再利用主成分分析对Contourlet系数进行适当地消噪处理.通过对OMIS图像的实验结果表明该方法能够同时消除高光谱多个波段图像中的噪声,从整体上改善高光谱图像质量,且性能上要优于PCA和Contourlet变换方法.
해문제출료일충괄합우고광보초유수거처리적기우Contourlet변환화주성분분석적조성소제방법.해방법수선이용Contourlet변환실현도상적희소표시,재이용주성분분석대Contourlet계수진행괄당지소조처리.통과대OMIS도상적실험결과표명해방법능구동시소제고광보다개파단도상중적조성,종정체상개선고광보도상질량,차성능상요우우PCA화Contourlet변환방법.
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.