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Semisupervised Hyperspectral Image Classification Using Soft Sparse Multinomial Logistic Regression
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DOI：10.1109/LGRS.2012.2205216.png)
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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.
Keyword:
Hyperspectral image classification
semisupervised learning (SSL)
soft labels
sparse multinomial logistic regression (SMLR)
unlabeled training samples
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