  This thesis proposes spatio-spectral techniques for hyperspectral image
analysis. 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 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.
