期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 49, 期 11, 页码 4263-4281出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2011.2163822
关键词
Hyperspectral imaging; scene classification; sparse modeling; spectral unmixing
类别
资金
- National Geospatial-Intelligence Agency
- Office of Naval Research
- Army Research Office
- National Science Foundation
- Defense Advanced Research Projects Agency
- Level Sets Systems
- Air Force Office of Scientific Research (NSSEFF)
- Division of Computing and Communication Foundations
- Direct For Computer & Info Scie & Enginr [1249263] Funding Source: National Science Foundation
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.
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