Most remote sensing data-sets contain a limiting number of independent spatial and spectral measurements,
beyond which no effective increase in information is achieved. This paper presents a Physically Motivated
Correlation Formalism (PMCF) ,which places both Spatial and Spectral data on an equivalent
mathematical footing in the context of a specific Kernel, such that, optimal combinations of independent
data can be selected from the entire Hypercube via the method of "Correlation Moments". We present an
experimental and computational analysis of Hyperspectral data sets using the Michigan Tech VFTHSI
[Visible Fourier Transform Hyperspectral Imager] based on a Sagnac Interferometer, adjusted to obtain
high SNR levels. The captured Signal Interferograms of different targets - aerial snaps of Houghton and
lab-based data (white light , He-Ne laser , discharge tube sources) with the provision of customized scan of
targets with the same exposures are processed using inverse imaging transformations and filtering
techniques to obtain the Spectral profiles and generate Hypercubes to compute Spectral/Spatial/Cross
Moments. PMCF answers the question of how optimally the entire hypercube should be sampled and
finds how many spatial-spectral pixels are required for a particular target recognition.
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