Abstract:
Integer-coefficient Discrete Wavelet Transformation (DWT) filters widely used in the literature are implemented and investigated as spectral decorrelator for on-board los...Show MoreMetadata
Abstract:
Integer-coefficient Discrete Wavelet Transformation (DWT) filters widely used in the literature are implemented and investigated as spectral decorrelator for on-board lossless hyperspectral image compression. As the performance of spectral decorrelation step has direct impact on the compression ratio (CR), it is important to employ the most convenient spectral decorrelator in terms of low computational complexity and high CR. Extensive tests using AVIRIS image data set are carried out and CRs corresponding to various subband decomposition levels are presented within a lossless hyperspectral compression framework. Results suggest that Cohen-Daubechies-Feauveau (CDF) 9/7 integer-coefficient wavelet transform with five levels of spectral subband decomposition would be an efficient spectral decorrelator for on-board lossless hyperspectral image compression.
Published in: 2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)
Date of Conference: 24-27 June 2014
Date Added to IEEE Xplore: 26 October 2017
ISBN Information:
Electronic ISSN: 2158-6276