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Spatial-spectral data fusion for resolution enhancement of hyperspectral imagery

A new spatial-spectral data fusion technique based on spectral mixture analysis and super-resolution mapping for spatial resolution enhancement of hyperspectral imagery is proposed in this paper. To this end, a linear mixture model and a constrained least squares based unmixing algorithm are applied for spectral unmixing of the hyperspectral imagery and the resulted fractional images are processed based on a spatial-spectral information correlation model through a super-resolution mapping technique. The obtained results validate the effectiveness of the method. It doesn't need any a priori information of the scene or secondary high resolution source of data, and is fast.