An adaptive two-stage KLT scheme for spectral decorrelation in hyperspectral bandwidth compression

Abstract

A computationally efficient adaptive 2-stage Karhunen-Loeve Transform (KLT) scheme for spectral decorrelation in hyperspectal lossy bandwidth compression is presented. The component decorrelation of the JPEG 2000 (extension 2) is replaced with the proposed adaptive 2-stage KLT spectral decorrelation scheme. Direct application of a single KLT across the entire set of hyperspectal imagery may not be computationally practical. The proposed scheme alleviates this problem by partitioning the spectral data set into small subsets. The spectral correlation within each partition is removed via the 1st-stage KLT operation. To remove the remaining inter-partition correlation, a 2nd-stage KLT is applied to the top few sets of eaui-level principal component (PC) images from the 1st-stage. The computation savings resulting from 2-stage KLT is parametrically quantified. The proposed adaptive 2-stage KLT uses only a fraction of the equi-level 1st-stage PC images in the 2nd-stage KLT process. This adaptive scheme results in reducing the size of the 2nd-stage KLT transformation matrices and further improvement in computational complexity and coding efficiency. It is shown that reconstructed image quality, as measured via statistical and/or machine-based exploitation measures, is improved by using a smaller partition size in the 1st-stage KLT. A criterion based on the components of the eigenvectors of the cross-covariance matrix is established to identify such 1st-stage PC images. The proposed adaptive spectral decorrelation scheme also reduces the overhead bits required to transmit the covariance matrices, or eigenvectors, along the coding bit stream to the receiver through the downlink channel.