1 November 1996 Fast three-dimensional data compression of hyperspectral imagery using vector quantization with spectral-feature-based binary coding
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Abstract
A fast lossy 3-D data compression scheme using vector quantization (VQ) is presented that exploits the spatial and the spectral redundancy in hyperspectral imagery. Hyperspectral imagery may be viewed as a 3-D array of samples in which two dimensions correspond to spatial position and the third to wavelength. Unlike traditional 2-D VQ, where 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-feature-based 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 by 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 signal-to-noise ratios of the reconstructed hyperspectral sequences exceeding 45.2 dB.
Shen-en Qian, Allan Bernard Hollinger, Daniel J. Williams, Davinder Manak, "Fast three-dimensional data compression of hyperspectral imagery using vector quantization with spectral-feature-based binary coding," Optical Engineering 35(11), (1 November 1996). https://doi.org/10.1117/1.601062
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