Journal
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Volume 48, Issue 7, Pages 2854-2863Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2010.2042063
Keywords
Component scalability; hyperspectral data coding; Karhunen-Loeve Transform (KLT); low cost; progressive lossy-to-lossless (PLL) and lossy compression
Categories
Funding
- Spanish Government
- Catalan Government
- FEDER [TIN2009-14426-C02-01, TIN2009-05737-E, SGR2009-1224, FPU2008]
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The Karhunen-Loeve transform (KLT) is widely used in hyperspectral image compression because of its high spectral decorrelation properties. However, its use entails a very high computational cost. To overcome this computational cost and to increase its scalability, in this paper, we introduce a multilevel clustering approach for the KLT. As the set of different multilevel clustering structures is very large, a two-stage process is used to carefully pick the best members for each specific situation. First, several candidate structures are generated through local search and eigenthresholding methods, and then, candidates are further screened to select the best clustering configuration. Two multilevel clustering combinations are proposed for hyperspectral image compression: one with the coding performance of the KLT but with much lower computational requirements and increased scalability and another one that outperforms a lossy wavelet transform, as spectral decorrelator, in quality, cost, and scalability. Extensive experimental validation is performed, with images from both the AVIRIS and Hyperion sets, and with JPEG2000, 3D-TCE, and CCSDS-Image Data Compression recommendation as image coders. Experiments also include classification-based results produced by k-means clustering and Reed-Xiaoli anomaly detection.
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