Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 10, Issue 5, Pages 1224-1228Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2012.2236818
Keywords
Domain adaptation (DA); hyperspectral image classification; maximum mean discrepancy (MMD); remote sensing; sample selection bias; support vector machines (SVMs)
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Funding
- Natural Science Foundation of China [40971245]
- European Space Agency-Ministry of Science and Technology Dragon 3 Cooperation Project [10689]
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This letter presents a novel semisupervised method for addressing a domain adaptation problem in the classification of hyperspectral data. To overcome the influence of distribution bias between the source and target domains, we introduce the domain transfer multiple-kernel learning to simultaneously minimize the maximum mean discrepancy criterion and the structural risk functional of support vector machines. Then, the pairwise binary classifiers are merged as the multiclass classifier for solving the classification problem in hyperspectral data. Both bias and non-bias sampling strategies are introduced to evaluate the robustness of the proposed method against the spectral distribution bias. The results obtained from real data sets show that the proposed method can achieve higher classification accuracy even with cross-domain distribution bias and provide robust solutions with different labeled and unlabeled data sizes.
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