Clustering Regression Wavelet Analysis for Lossless Compression of Hyperspectral Imagery
AffiliationUniv Arizona, Dept Elect & Comp Engn
Univ Arizona, Dept Biomed Engn
MetadataShow full item record
CitationAhanonu, E., Marcellin, M., & Bilgin, A. (2019, March). Clustering Regression Wavelet Analysis for Lossless Compression of Hyperspectral Imagery. In 2019 Data Compression Conference (DCC) (pp. 551-551). IEEE.
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at firstname.lastname@example.org.
AbstractRecently, Regression Wavelet Analysis (RWA) was proposed as a method for lossless compression of hyperspectral images. In RWA, a linear regression is performed after a spectral wavelet transform to generate predictors which estimate the detail coefficients from approximation coefficients at each scale of the spectral wavelet transform. In this work, we propose Clustering Regression Wavelet Analysis (RWA-C), an extension of the original ‘Restricted’ RWA model which may be used to improve compression performance while maintaining component scalability. We demonstrate that clustering may be used to group pixels with similar spectral profiles, these clusters may then be more efficiently processed to improve RWA prediction performance while only requiring a modest increase side-information.
VersionFinal accepted manuscript