A novel semisupervised SVM for pixel classification of remote sensing imagery

This article introduces a semisupervised support vector machine classification technique that exploits both labeled and unlabeled points for addressing the problem of pixel classification of remote sensing images. The proposed technique is based on applying the margin maximization principle to both labeled and unlabeled patterns. Semisupervised SVM progressively searches a reliable discriminant hyperplane in the high dimensional space through iterative method exploiting both labeled and unlabeled samples. In particular, the dynamic thresholding and successive filtering of the unlabeled set are exploited to find a reliable separating hyperplane. The proposed technique is first demonstrated for six labeled datasets described in terms of feature vectors and then identifying different land cover regions in remote sensing imagery and compared with the standard SVM. Experimental results confirm that employing this learning scheme removes unnecessary points to a great extent from the unlabeled set and increases the accuracy level on the other hand. Comparison is made in terms of accuracy, ROC, AUC and F-measure for the labeled data and quantitative cluster validity indices as well as classified image quality for the image data.