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Learning discriminative sparse representations for modeling, source separation, and mapping of hyperspectral imagery

Publication ,  Journal Article
Castrodad, A; Xing, Z; Greer, JB; Bosch, E; Carin, L; Sapiro, G
Published in: IEEE Transactions on Geoscience and Remote Sensing
November 1, 2011

A method is presented for subpixel modeling, 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, allowing for linear mixture analysis. This model provides flexibility in source representation and selection, thus accounting for spectral variability, small-magnitude errors, and noise. A spatial-spectral coherence regularizer in the optimization allows pixel classification to be influenced by similar neighbors. We extend the proposed approach for cases for which there is no knowledge of the materials in the scene, unsupervised classification, and provide experiments and comparisons with simulated and real data. We also present results when the data have been significantly undersampled and then reconstructed, still retaining high-performance classification, showing the potential role of compressive sensing and sparse modeling techniques in efficient acquisition/transmission missions for hyperspectral imagery. © 2006 IEEE.

Duke Scholars

Published In

IEEE Transactions on Geoscience and Remote Sensing

DOI

ISSN

0196-2892

Publication Date

November 1, 2011

Volume

49

Issue

11 PART 1

Start / End Page

4263 / 4281

Related Subject Headings

  • Geological & Geomatics Engineering
  • 40 Engineering
  • 37 Earth sciences
  • 0909 Geomatic Engineering
  • 0906 Electrical and Electronic Engineering
  • 0404 Geophysics
 

Citation

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Castrodad, A., Xing, Z., Greer, J. B., Bosch, E., Carin, L., & Sapiro, G. (2011). Learning discriminative sparse representations for modeling, source separation, and mapping of hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 49(11 PART 1), 4263–4281. https://doi.org/10.1109/TGRS.2011.2163822
Castrodad, A., Z. Xing, J. B. Greer, E. Bosch, L. Carin, and G. Sapiro. “Learning discriminative sparse representations for modeling, source separation, and mapping of hyperspectral imagery.” IEEE Transactions on Geoscience and Remote Sensing 49, no. 11 PART 1 (November 1, 2011): 4263–81. https://doi.org/10.1109/TGRS.2011.2163822.
Castrodad A, Xing Z, Greer JB, Bosch E, Carin L, Sapiro G. Learning discriminative sparse representations for modeling, source separation, and mapping of hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing. 2011 Nov 1;49(11 PART 1):4263–81.
Castrodad, A., et al. “Learning discriminative sparse representations for modeling, source separation, and mapping of hyperspectral imagery.” IEEE Transactions on Geoscience and Remote Sensing, vol. 49, no. 11 PART 1, Nov. 2011, pp. 4263–81. Scopus, doi:10.1109/TGRS.2011.2163822.
Castrodad A, Xing Z, Greer JB, Bosch E, Carin L, Sapiro G. Learning discriminative sparse representations for modeling, source separation, and mapping of hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing. 2011 Nov 1;49(11 PART 1):4263–4281.

Published In

IEEE Transactions on Geoscience and Remote Sensing

DOI

ISSN

0196-2892

Publication Date

November 1, 2011

Volume

49

Issue

11 PART 1

Start / End Page

4263 / 4281

Related Subject Headings

  • Geological & Geomatics Engineering
  • 40 Engineering
  • 37 Earth sciences
  • 0909 Geomatic Engineering
  • 0906 Electrical and Electronic Engineering
  • 0404 Geophysics