Analysis of Inter-band Spectral Cross-Correlation Structure of Hyperspectral Data

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 spaceborne 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 exploit the similarities in spectral dimensions; which require bands reordering or regrouping, to make use of the spectral redundancy. In this paper, we analyze the spectral cross correlation between bands for AVIRIS and Hyperion hyperspectral data; spectral cross correlation matrix is calculated, analyzed, and by assessing the strength of the spectral matrix, we propose a new technique for bands regroup by finding the highly correlated groups of bands in the hyperspectral data cube based on "inter band correlation square". Key-Words: -hyperspectral imaging, bands regrouping, edge detection, spectral correlation matrix, image compression