Discriminative sparse representations in hyperspectral imagery

Alexey Castrodad, Zhengming Xing, John Greer, Edward Bosch, Lawrence Carin, Guillermo Sapiro

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Scopus citations


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 original image samples. The proposed supervised HSI classification is performed using a measure that accounts for both reconstruction errors and sparsity levels for sparse representations based on class-dependent learned dictionaries. Combining the dictionaries learned for the different materials, a linear mixing model is derived for sub-pixel classification. Results with real hyperspectral data cubes are shown both for urban and non-urban terrain. © 2010 IEEE.
Original languageEnglish (US)
Title of host publicationProceedings - International Conference on Image Processing, ICIP
Number of pages4
StatePublished - Dec 1 2010
Externally publishedYes


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