Hyperspectral Data Compression Based On Weighted Prediction

Hyperspectral imaging has been widely studied in many applications; notably in climate changes, vegetation, and desert studies. However, such kind of imaging brings a huge amount of data, which requires transmission, processing, and storage resources for both airborne and space borne imaging. Compression of hyperspectral data cubes is an effective solution for these problems. Lossless compression of the hyperspectral data usually results in low compression ratio, which may not meet the available resources; on the other hand, lossy compression may give the desired ratio, but with a significant degradation effect on object identification performance of the hyperspectral data. Moreover, most hyperspectral data compression techniques exploits the similarities in spectral dimensions; which requires bands reordering or regrouping, to make use of the spectral redundancy. In this paper, we analyse the spectral cross correlation between bands for Hyperion hyperspectral data; spectral cross correlation matrix is calculated, assessing the strength of the spectral matrix, and finally, we propose new technique to find highly correlated groups of bands in the hyperspectral data cube based on "inter band correlation square", from the resultant groups of bands we propose a new predictor that can predict efficiently the whole bands within data cube based on weighted combination of spectral and spatial prediction, the results are evaluated versus other state of the art predictor for lossless compression.