A self-trained ensemble with semisupervised SVM: An application to pixel classification of remote sensing imagery
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- 作者：Ujjwal Maulik ; Debasis Chakraborty
- 关键词：Semisupervised learning ; Support vector machines ; Remote sensing satellite images ; Quadratic programming ; Self-training ; Classifier ensemble
- 刊名：Pattern Recognition
- 出版年：2011
- 出版时间：March 2011
- 年：2011
- 卷：44
- 期：3
- 页码：615-623
- 全文大小：1140 K
文摘
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 (S3VMs) 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.