Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 10, Issue 2, Pages 318-322Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2012.2205216
Keywords
Hyperspectral image classification; semisupervised learning (SSL); soft labels; sparse multinomial logistic regression (SMLR); unlabeled training samples
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Funding
- European Community's Marie Curie Research Training Networks Programme [MRTNCT-2006-035927]
- Portuguese Science and Technology Foundation [PEst-OE/EEI/LA0008/2011]
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In this letter, we propose a new semisupervised learning (SSL) algorithm for remotely sensed hyperspectral image classification. Our main contribution is the development of a new soft sparse multinomial logistic regression model which exploits both hard and soft labels. In our terminology, these labels respectively correspond to labeled and unlabeled training samples. The proposed algorithm represents an innovative contribution with regard to conventional SSL algorithms that only assign hard labels to unlabeled samples. The effectiveness of our proposed method is evaluated via experiments with real hyperspectral images, in which comparisons with conventional semisupervised self-learning algorithms with hard labels are carried out. In such comparisons, our method exhibits state-of-the-art performance.
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