Hyperspectral bands prediction based on inter-band spectral correlation structure

Ayman M. Ahmed; Mohamed El. Sharkawy; Salwa H. Elramly

doi:10.1117/12.2000559

19 February 2013 Hyperspectral bands prediction based on inter-band spectral correlation structure

Ayman M. Ahmed, Mohamed El. Sharkawy, Salwa H. Elramly

Proceedings Volume 8655, Image Processing: Algorithms and Systems XI; 86550Y (2013) https://doi.org/10.1117/12.2000559
Event: IS&T/SPIE Electronic Imaging, 2013, Burlingame, California, United States

Abstract

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 exploits the similarities in spectral dimensions; which requires 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, assessing the strength of the spectral matrix, we propose new technique to find highly correlated groups of bands in the hyperspectral data cube based on "inter band correlation square", and finally, we propose a new technique of band regrouping based on correlation values weights for different group of bands as network of correlation.

Citation Download Citation

Ayman M. Ahmed, Mohamed El. Sharkawy, and Salwa H. Elramly "Hyperspectral bands prediction based on inter-band spectral correlation structure", Proc. SPIE 8655, Image Processing: Algorithms and Systems XI, 86550Y (19 February 2013); https://doi.org/10.1117/12.2000559

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KEYWORDS

Particle filters

Matrices

Image compression

Hyperspectral imaging

Image processing

Edge detection

Data compression

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