期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 53, 期 3, 页码 1346-1354出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2014.2337883
关键词
Binary hypothesis; hyperspectral imagery; sparse representation; target detection
类别
资金
- National Basic Research Program of China (973 Program) [2012CB719905, 2011CB707105]
- National Natural Science Foundation of China [61102128]
- Fundamental Research Funds for the Central Universities [211-274175]
In this paper, a new sparse representation-based binary hypothesis (SRBBH) model for hyperspectral target detection is proposed. The proposed approach relies on the binary hypothesis model of an unknown sample induced by sparse representation. The sample can be sparsely represented by the training samples from the background-only dictionary under the null hypothesis and the training samples from the target and background dictionary under the alternative hypothesis. The sparse vectors in the model can be recovered by a greedy algorithm, and the same sparsity levels are employed for both hypotheses. Thus, the recovery process leads to a competition between the background-only subspace and the target and background subspace, which are directly represented by the different hypotheses. The detection decision can be made by comparing the reconstruction residuals under the different hypotheses. Extensive experiments were carried out on hyperspectral images, which reveal that the SRBBH model shows an outstanding detection performance.
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