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

This paper describes a spectral index (SI)-based multiple subcodebook algorithm (MSCA) for lossy hyperspectral data compression. The scene of a hyperspectral dataset to be compressed is delimited into n regions by segmenting its SI image. The spectra in each region have similar spectral characteristics. The dataset is then separated into n subsets, corresponding to the n regions. While keeping the total number of codevectors the same (i.e. the same compression ratio), not just a single codebook, but n smaller and more efficient subcodebooks are generated. Each subcodebook is used to compress the spectra in the corresponding region. With the MSCA, both the codebook generation time (CGT) and coding time (CT) can be improved by a factor of around n at almost no loss of fidelity. Four segmentation methods for delimiting the scene of the data cube were studied. Three hyperspectral vector quantization data compression systems that use the improved techniques were simulated and tested. The simulation results show that the CGT could be reduced by more than three orders of magnitude, while the quality of the codebooks remained good. The overall processing speed of the compression systems could be improved by a factor of around 1000 at an average fidelity penalty of 1.0 dB