Vector quantization using spectral index-based multiple subcodebooks for hyperspectral data compression

This paper describes a spectral index (SI)-based multiple subcodebook algor ithm (MSCA) for lossy hyperspectral data compression. The scene of a hypers pectral dataset to be compressed is delimited into n regions by segmenting its SI image. The spectra in each region have similar spectral characterist ics. The dataset is then separated into n subsets, corresponding to the n r egions. While keeping the total number of codevectors the same (i.e. the sa me compression ratio), not just a single codebook, but n smaller and more e fficient subcodebooks are generated. Each subcodebook is used to compress t he spectra in the corresponding region. With the MSCA, both the codebook ge neration time (CGT) and coding time (CT) carl be improved by a factor of ar ound n at almost no loss of fidelity, Four segmentation methods for delimit ing the scene of the data cube were studied. Three hyperspectral vector qua ntization data compression systems that use the improved techniques were si mulated and tested. The simulation results show that the CCT could he reduc ed by more than three orders of magnitude, while the quality of the codeboo ks remained good. The overall professing speed of the compression systems c ould be improved by a factor of around 1000 at an average fidelity penalty of 1.0 dB.