Bandwidth compression of hyperspectral imagery data using a simplified KLT/JPEG 2000 approach

Abstract

A viable lossy bandwidth compression for hyperspectral imagery is presented. The algorithm is leveraged on the standard JPEG 2000 technology. The component decorrelation of the JPEG 2000 (extension 2) is replaced with a two-level Karhunen-Loeve Transformation (KLT) operation resulting in a reduction in the computation complexity. The set of <i>n</i>² hyperspectral imagery is arranged as an <i>n</i> by <i>n</i> mosaic. Each of the <i>n</i> columns of the mosaic is spectrally uncorrelated via a first-level KLT operation. The resulting <i>n</i> principal component (PC) images for each column are placed next to one another to form an <i>n</i> by <i>n</i> mosaic of PC images. A second-level KLT is then applied to the first four rows of the <i>n</i> by <i>n</i> PC mosaic to approximate a full spectral decorrelation. This approach reduces the computational complexity of the KLT spectral decorrelation process of JPEG 2000 since, 1) it uses a smaller and computationally more feasible KLT matrix (i.e., <i>n</i> by <i>n</i> KLT matrix instead of size <i>n</i>² by <i>n</i>²) and 2) it reduces the number of required computations for spectral decorrelation by a factor of <i>n/4</i>.