Compression of Hyperspectral Imagery

High dimensional source vectors, such as occur in hyperspectral imagery, are partitioned into a number of subvectors of (possibly) different length and then each subvector is vector quantized (VQ) individually with an appropriate codebook. A locally adaptive partitioning algorithm is introduced that performs comparably in this application to a more expensive globally optimal one that employs dynamic programming. The VQ indices are entropy coded and used to condition the lossless or near-lossless coding of the residual error. Motivated by the need of maintaining uniform quality across all vector components, a Percentage Maximum Absolute Error distortion measure is employed. Experiments on the lossless and near-lossless compression of NASA AVIRIS images are presented. A key advantage of our approach is the use of independent small VQ codebooks that allow fast encoding and decoding.