3D data compression of hyperspectral imagery using vector quantization with NDVI-based multiple codebooks

This paper describes a new vector quantization based algorithm that uses the remote sensing knowledge Normalized Difference Vegetation Index (NDVI) to reduce the codebook generation time (CGT) and coding time (CT). The experimental results showed that it yielded an improvement in both CGT and CT of 14.1 and 14.8 times when the scene of a data set is segmented into 16 classes, while the reconstruction fidelity was almost as same as that by the conventional vector quantization algorithm. The PSNR of the reconstructed data reached 43.31 dB when the compression ratio was of 81:1.