Discriminative Sparse Representations in Hyperspectral Imagery
Abstract
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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 01, 2010
- Accession Number
- ADA519659
Entities
People
- Alexey Castrodad
- Edward Bosch
- Guillermo Sapiro
- John Greer
- Lawrence Carin
- Zhengming Xing
Organizations
- University of Minnesota