期刊
INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS
卷 22, 期 4, 页码 438-448出版社
SAGE PUBLICATIONS LTD
DOI: 10.1177/1094342007088380
关键词
principal component analysis; hyperspectral image compression; JPEG2000; spectral decorrelation; anomaly detection
Principal component analysis (PCA) is an effective tool for spectral decorrelation of hyperspectral imagery, and PCA-based spectral transforms have been employed successfully in conjunction with JPEG2000 for hyperspectral image compression. However, the computational cost of determining the data-dependent PCA transform is high because of its traditional eigendecomposition implementation which requires calculation of a covariance matrix across the data. Several strategies for reducing the computation burden of PCA are explored, including both spatial and spectral sub-sampling in the covariance calculation as well as an iterative algorithm that circumvents determination of the covariance matrix entirely. Experimental results investigate the impacts of such low-complexity PCA on JPEG2000 compression of hyperspectral images, focusing on rate-distortion performance as well as data-analysis performance at an anomaly-detection task.
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