Spectral Representation via Data-Guided Sparsity for Hyperspectral Image Super-Resolution

Sparse Spatio-spectral Representation for Hyperspectral Image Super-resolution

Existing hyperspectral imaging systems produce low spatial resolution images due to hardware constraints. We propose a sparse representation based approach for hyperspectral image super-resolution. The proposed approach first extracts distinct reflectance spectra of the scene from the available hyperspectral image. Then, the signal sparsity, non-negativity and the spatial structure in the scene...

Spectral Super-Resolution for Hyperspectral Images via Sparse Representations

The spectral dimension of hyperspectral imaging (HSI) systems plays a fundamental role in numerous terrestrial and earth observation applications, including spectral unmixing, target detection, and classification among others. However, in several cases the spectral resolution of HSI systems is sacrificed for the shake of spatial resolution, as such in the case of snapshot spectral imaging syste...

Hyperspectral Super-Resolution with Spectral Unmixing Constraints

Hyperspectral sensors capture a portion of the visible and near-infrared spectrum with many narrow spectral bands. This makes it possible to better discriminate objects based on their reflectance spectra and to derive more detailed object properties. For technical reasons, the high spectral resolution comes at the cost of lower spatial resolution. To mitigate that problem, one may combine such ...

Tensor Block-Sparsity Based Representation for Spectral-Spatial Hyperspectral Image Classification

Zhi He 1,*, Jun Li 1 and Lin Liu 1,2 1 Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, Center of Integrated Geographic Information Analysis, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China; [email protected] (J.L.); [email protected] (L.L.) 2 Department of Geography, University of Cincinnati (UC), Cincinnati, OH 45221, USA ...

Selected Paper : Image Super - Resolution via Sparse Representation Authors :