期刊
PATTERN RECOGNITION
卷 44, 期 3, 页码 615-623出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2010.09.021
关键词
Semisupervised learning; Support vector machines; Remote sensing satellite images; Quadratic programming; Self-training; Classifier ensemble
In this article, we present a semisupervised support vector machine that uses self-training approach. We then construct an ensemble of semisupervised SVM classifiers to address the problem of pixel classification of remote sensing images. Semisupervised support vector machines ((SVMs)-V-3) are based on applying the margin maximization principle to both labeled and unlabeled samples. The ensemble of SVM classifiers recognizes the conceptual similarity between component classifiers from the same data source. The effectiveness of the proposed technique is first demonstrated for two numeric remote sensing data described in terms of feature vectors and then identifying different land cover regions in remote sensing imagery. Experimental results on these datasets show that employing this learning scheme can increase the accuracy level. The performance of the ensemble is compared with one of its component classifier and conventional SVM in terms of accuracy and quantitative cluster validity indices. (C) 2010 Elsevier Ltd. All rights reserved.
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