Fast threedimensional data compression of hyperspectral imagery using vector quantization with spectralfeaturebased binary coding
Abstract
A fast lossy 3D data compression scheme using vector quantization (VQ) is presented that exploits the spatial and the spectral redundancy in hyperspectral imager. Hyperspectral imagery may be viewed as a 3D array of samples in which two dimensions correspond to spatial position and the third to wavelength. Unlike traditional 2D VQ, spatial blocks of n X m pixels are taken as vectors, we define one spectrum, corresponding to a profile taken along the wavelength axis, as a vector. This constitution of vectors makes good use of the high correlation in the spectral domain and achieves a high compression ratio. It also leads to fast codebook generation and fast codevector matching. A coding scheme for fast vector matching called spectral featurebased binary coding (SFBBC) is used to encode each spectral vector into a simple and efficient set of binary codes. The generation of the codebook and the matching of codevectors are performed y matching the binary codes produced by the SFBBC. The experiments were carried out using a test hyperspectral data cube from the Compact Airborne Spectrographic imager. Generating a codebook is 39 times faster with the SFBBC than with conventional VQ, and the data compression is 30 to 40 times faster. Compression ratios greater than 192:1 have been achieved with peak signaltonoise ratios of the reconstructed hyperspectral sequences exceeding 45.2 dB.
 Publication:

Optical Engineering
 Pub Date:
 November 1996
 DOI:
 10.1117/1.601062
 Bibcode:
 1996OptEn..35.3242Q