Discriminative sparse representations in hyperspectral imagery

Alexey Castrodad, Zhengming Xing, John Greer, Edward Bosch, Lawrence Carin, Guillermo Sapiro
2010 2010 IEEE International Conference on Image Processing  
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
more » ... parsity 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.
doi:10.1109/icip.2010.5651568 dblp:conf/icip/CastrodadXGBCS10 fatcat:oym7waumuzfodmqvbg5xht5oam