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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...
Spatial resolution is one of the most expensive and hardest to improve in imaging systems. An efficient spatial-spectral data fusion method for superresolution of hyperspectral (HS) imagery through exploiting the spatial correlation of the endmembers using a superresolution mapping (SRM) technique is proposed in this paper. Endmember abundances (fractional images) obtained using linear mixture model...
In this paper an improved supervised classification technique through applying a superresolution method on remotely sensed hyperspectral images is introduced. Superresolution methods provide high-resolution images from a sequence of low-resolution frames. In the proposed technique, classification of the hyperspectral image is carried out using spectrally homogenous training classes of pixels. Low...
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