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A fast and efficient spatial-spectral fusion method for resolution enhancement of hyperspectral imagery is proposed in this paper. A linear mixture model and fully constrained least squares based unmixing algorithm are applied for spectral unmixing of the hyperspectral imagery and the resulted fractional images are processed using a spatial-spectral information correlation model through a learning-based...
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...
Hyperspectral technology has introduced a new perspective in remote sensing applications but suffers from low spatial resolution. 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 fully constrained least squares based...
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