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Segmented principal component analysis for parallel compression of hyperspectral imagery (2009)
| Content Provider | CiteSeerX |
|---|---|
| Author | Du, Qian Zhu, Wei Yang, He Fowler, James E. |
| Abstract | Abstract—Principal component analysis (PCA) is widely used for spectral decorrelation in the JPEG2000 compression of hyperspectral imagery. However, due to the data-dependent nature of principal components, the principal component transform matrix is stored in the JPEG2000 bitstream, constituting an overhead that is often negligible if the spatial size of the image is large. However, in parallel compression in which the data set is partitioned to multiple independent processing nodes, the overhead may no longer remain negligible. It is shown that a segmented approach to PCA can greatly mitigate the detrimental effects of transform-matrix overhead and can outperform wavelet-based decorrelation which entails no such overhead. Index Terms—Hyperspectral compression, principal component analysis (PCA), spectral segmentation. I. |
| File Format | |
| Journal | IEEE Geosci. Remote Sens. Lett |
| Language | English |
| Publisher Date | 2009-01-01 |
| Access Restriction | Open |
| Subject Keyword | Hyperspectral Imagery Parallel Compression Principal Component Analysis Data Set Spectral Segmentation Detrimental Effect Principal Component Transform Matrix Jpeg2000 Bitstream Wavelet-based Decorrelation Jpeg2000 Compression Transform-matrix Overhead Abstract Principal Component Analysis Data-dependent Nature Principal Component Segmented Approach Independent Processing Node Spatial Size Index Term Hyperspectral Compression Spectral Decorrelation |
| Content Type | Text |
| Resource Type | Article |
