Granulation-based self-training for the semi-supervised classification of remote-sensing images

Collection of quality-labeled training samples in the area of remote sensing is very difficult, costly, time-consuming, and tedious due to various constraints. Classification of remote-sensing images is a challenging task due to the limited availability of quality-labeled samples for the training process. To solve the problem of labeled samples, various semi-supervised techniques have been designed and explored for the classification of remote-sensing images. Self-training is a popular semi-supervised method widely used for the training of supervised classifier with limited labeled and a large pool of unlabeled samples. However, the traditional self-training approach gives poor performance for the classification of remote-sensing images. The traditional self-training method selects samples only on the basis of maximum classification probability criterion which may not improve the classifier accuracy. The effectiveness of the classifiers trained in the self-training fashion depends on the selection of correct, diverse, and informative samples for the labeled training set. In this paper, granular computing concepts have been utilized to improve the self-training approach for the classification of the remote-sensing images. The proposed approach first groups the unlabeled samples into several numbers of granules. After that, a supervised classifier is trained with few labeled samples and the trained classifier is used to select the most confident granules set. The selected most confident granules help to add qualitative samples into the labeled set for the effective training of the classifiers. The experimental results with three benchmark remote-sensing data sets show that the proposed method has produced improvement in the classification accuracy.