Autocorrelogram approach for estimating the spectral-spatial variability of hyperspectral images

Details Autocorrelogram approach for estimating the spectral-spatial variability of hyperspectral images Autocorrelogram approach for estimating the spectral-spatial variability of hyperspectral images

Oct 1999

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1999spie.3753..450f&link_type=abstract

Proc. SPIE Vol. 3753, p. 450-457, Imaging Spectrometry V, Michael R. Descour; Sylvia S. Shen; Eds.

Physics

Scientific paper

The amount of information contained in a single hyperspectral image is overwhelming for the human operator. As a result, assessing the spatial and spectral variability of a hyperspectral image is very difficult. The existing techniques mainly rely on different preprocessing algorithms that reduce the high-dimensionality of the hyperspectral data down to a few images that can be visualized using traditional RGB or RGBI combinations. The proposed auto-correlogram approach provides a simple framework for reducing a hyperspectral image cube to a single grayscale image that is easy to interpret and screen for spectral anomalies.

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