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— Non-linear dimensionality reduction and vector segmentation of hyper-spectral images is investigated in this letter. The proposed framework takes into account the non-linear nature of high dimensional hyper-spectral images, and projects onto a lower dimensional space via a spatially-coherent locally linear embedding technique. The spatial coherence is(More)
Recent advances in sparse modeling and dictionary learning for discriminative applications show high potential for numerous classification tasks. In this paper, we show that highly accurate material classification from hyperspectral imagery (HSI) can be obtained with these models, even when the data is reconstructed from a very small percentage of the(More)
A method is presented for sub-pixel mapping and classification in hyperspectral imagery, using learned block-structured discriminative dictionaries, where each block is adapted and optimized to represent a material in a compact and sparse manner. The spectral pixels are modeled by linear combinations of subspaces defined by the learned dictionary atoms,(More)
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