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LOW-COMPLEXITY PRINCIPAL COMPONENT ANALYSIS FOR HYPERSPECTRAL IMAGE COMPRESSION
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DOI：10.1177/1094342007088380.png)
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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.
Keyword:
principal component analysis
hyperspectral image compression
JPEG2000
spectral decorrelation
anomaly detection
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2.5
论文数:
1.1K
被引数:
1.3K
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