期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 39, 期 12, 页码 2664-2679出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/36.975001
关键词
adaptive iterative classifier; high-dimensional data; labeled samples; limited training data set; semilabeled samples
In this paper, we propose a self-learning and self-improving adaptive classifier to mitigate the problem of small training sample size that can severely affect the recognition accuracy of classifiers when the dimensionality of the multispectral data is high. This proposed adaptive classifier utilizes classified samples (referred as semilabeled samples) in addition to original training samples iteratively. In order to control the influence of semilabeled samples, the proposed method gives full weight to the training samples and reduced weight to semilabeled samples. We show that by using additional semilabeled samples that are available without extra cost, the additional class label information may be extracted and utilized to enhance statistics estimation and hence improve the classifier performance, and therefore the Hughes phenomenon (peak phenomenon) may be mitigated. Experimental results show this proposed adaptive classifier can improve the classification accuracy as well as representation of estimated statistics significantly.
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