Traditional image processing techniques used for 3- and 4- band images are not suited to the many-band character of spectral images. A sparse multi-dimensional lookup table with inter-node interpolation is a typical image processing technique used for applying either a known model or an empirically derived mapping to an image. Such an approach for spectral images becomes problematic because input dimensionality of lookup tables is proportional to the number of source image bands and the size of lookup tables is exponentially related to the number of input dimensions. While an RGB or CMY source image would require a 3-dimensional lookup table, a 31-band spectral image would need a 31-dimensional lookup table. A 31-dimensional lookup table would be absurdly large. A novel approach to spectral image processing is explored. This approach combines a low-cost spectral analysis followed by application of one from a set of low-dimensional lookup tables. The method is computationally feasible and does not make excessive demands on disk space or run-time memory.
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