A novel wavelet-based scheme to increase coefficient
independence in hyperspectral images is introduced for lossless
coding. The proposed regression wavelet analysis (RWA) uses
multivariate regression to exploit the relationships among wavelettransformed
components. It builds on our previous nonlinear
schemes that estimate each coefficient from neighbor coefficients.
Specifically, RWA performs a pyramidal estimation in the wavelet
domain, thus reducing the statistical relations in the residuals
and the energy of the representation compared to existing
wavelet-based schemes. We propose three regression models to
address the issues concerning estimation accuracy, component
scalability, and computational complexity. Other suitable regression
models could be devised for other goals. RWA is invertible, it
allows a reversible integer implementation, and it does not expand
the dynamic range. Experimental results over a wide range of
sensors, such as AVIRIS, Hyperion, and Infrared Atmospheric
Sounding Interferometer, suggest that RWA outperforms not only
principal component analysis and wavelets but also the best and
most recent coding standard in remote sensing, CCSDS-123.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/621311 |
| Date | 08 May 2016 |
| Creators | Marcellin, Michael W., Amrani, Naoufal, Serra-Sagristà. Joan, Laparra, Valero, Malo, Jesus |
| Contributors | University of Arizona, Universitat Autònoma de Barcelona, Barcelona, Spain, Universitat de València, València, Spain |
| Publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| Source Sets | University of Arizona |
| Language | English |
| Detected Language | English |
| Type | Article |
| Rights | © 2016 IEEE. |
| Relation | http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7487041&tag=1 |
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