Clustering-based extraction of near border training samples for classification of remote sensing image

In this paper, we investigate the joint use of modified k-means clustering and selecting a set of data near the decision remote sensing image with support vector machine (SVM). We propose to find informative samples and label them as near border training samples, which are induced by cluster center and cosine similarity measure between these data points and cluster center. The proposed approach works in two consecutive steps and near border training samples can be first produced in the clustering step and then are fed to SVM classification. A comparison with cluster centers, random sampling is provided, suggesting the location of labeled data within individual clusters experiments on real remote sensing images show a better classification accuracy of proposed method compared to state-of-the-art methods.