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Hyperspectral imaging and analysis for sparse reconstruction and recognition
Hyperspectral imaging, also known as imaging spectroscopy, captures data cube of a scene in two spatial and one spectral dimension. Hyperspectral image analysis refers to the operations that lead to the extraction of meaningful information from hyperspectral images. The huge spectral dimension of hyperspectral images is a bottleneck for efficient imaging and accurate image analysis. This thesis contributes to hyperspectral imaging and analysis methods at multiple levels.
Adaptive spatio-spectral support and variable exposure hyperspectral imaging is demonstrated to improve spectral reflectance recovery from hyperspectral images. Novel spectral dimensionality reduction techniques have been proposed from the perspective of spectral only and spatio-spectral information preservation. It was found that the joint sparse and joint group sparse hyperspectral image models achieve lower reconstruction error and higher recognition accuracy using only a small subset of the bands. Hyperspectral image databases have been developed and made publicly available for further research in compressed hyperspectral imaging, forensic document analysis and spectral reflectance recovery.
This research proposes novel spectral imaging and analysis methods to reduce the costs and improve the efficiency of image acquisition.