Abstract: Hyperspectral images provide higher spectral resolution than typical RGB images by including per-pixel irradiance measurements in a number of narrow bands of wavelength in the visible spectrum. The additional spectral resolution may be useful for many visual tasks, including segmentation, recognition, and relighting. Vision systems that seek to capture and exploit hyperspectral data should benefit from statistical models of natural hyperspectral images, but at present, relatively little is known about their structure. Using a new collection of fifty hyperspectral images of indoor and outdoor scenes, we derive an optimized "spatio-spectral basis" for representing hyperspectral image patches, and explore statistical models for the coefficients in this basis.
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