摘要
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。
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.
出处
《电子与信息学报》
EI
CSCD
北大核心
2009年第12期2892-2896,共5页
Journal of Electronics & Information Technology
基金
国家自然科学基金(60802084)资助课题
关键词
图像处理
高光谱遥感
去噪
CONTOURLET变换
主成分分析
Image processing
Hyperspectral remote sensing
Denoising
Contourlet transform
Principal Component Analysis （PCA）
