Discriminative sparse representations in hyperspectral imagery

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.

Full Text

Duke Authors

Cited Authors

  • Castrodad, A; Xing, Z; Greer, J; Bosch, E; Carin, L; Sapiro, G

Published Date

  • 2010

Published In

Start / End Page

  • 1313 - 1316

International Standard Serial Number (ISSN)

  • 1522-4880

Digital Object Identifier (DOI)

  • 10.1109/ICIP.2010.5651568