Material specific multiple observation resolution enhancement of hyperspectral imagery

In [1] we proposed a hyperspectral imaging model that represents spectral observations at different wavelengths as weighted linear combinations of a small number of basis images obtained through principal component analysis (PCA). Based on this imaging model we formulated a multiple observation resolution enhancement method for hyperspectral imagery. In this work we focus on material specific resolution enhancement. We start with pointing out a shortcoming of the PCA based imaging model. We then introduce modifications to integrate linear spectral mixing into the imaging model. Based on the updated model we introduce and implement material specific multiple observation resolution enhancement for hyperspectral imagery. We test the proposed method on AVIRIS data, and present numeric and visual results.