Recent advances in optical sensing technology (miniaturization and low-cost architectures for spectral imaging) and sensing platforms from which such imagers can be deployed have the potential to enable ubiquitous multispectral and hyperspectral imaging on demand in support of a variety of applications, including remote sensing and biomedicine. Often, however, robust analysis with such data is challenging due to limited/noisy ground-truth, and variability due to illumination, scale and acquisition conditions. In this talk, I will review recent advances in: (1) Subspace learning for learning illumination invariant discriminative subspaces from high dimensional hyperspectral imagery; (2) Semi-supervised and active learning for image analysis with limited ground truth; and (3) Deep learning variants that learn the spatial-spectral information in multi-channel optical data effectively from limited ground truth, by leveraging the structural information available in the unlabeled samples as well as the underlying structured sparsity of the data.
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