Low-Complexity Principal Component Analysis for Hyperspectral Image Compression
نویسندگان
چکیده
منابع مشابه
Low-Complexity Principal Component Analysis for Hyperspectral Image Compression
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 due to its traditional eigendecomposition implementation which requi...
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This article deals with the issue of reducing the spectral dimension of a hyperspectral image using principal component analysis (PCA). To perform this dimensionality reduction, we propose the addition of spatial information in order to improve the features that are extracted. Several approaches proposed to add spatial information are discussed in this article. They are based on mathematical mo...
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ژورنال
عنوان ژورنال: The International Journal of High Performance Computing Applications
سال: 2008
ISSN: 1094-3420,1741-2846
DOI: 10.1177/1094342007088380