A Novel Synergetic Classification Approach for Hyperspectral and Panchromatic Images Based on Self-Learning

In this paper, we propose a self-learning approach for remote sensing image classification. The main work of this paper aims at providing a new framework of semisupervised learning technique for multiple-source synergetic classification, thereby improving the classification accuracy under the condition of small samples. Considering the high spectral resolution of a hyperspectral (HS) image and the high spatial resolution of a panchromatic (PAN) image, the proposed approach combines image segmentation with an active learning algorithm and adopts a standard active learning method for a self-learning strategy, in which the learning algorithm automatically selects informative unlabeled samples by itself according to their collaborative spatial-spectral features and the predicted information of a spectral-based classifier. This way, no extra cost of human expertise is required for labeling the selected pixels when compared with conventional active learning methods. Experiments on three data sets, including HS and PAN images, indicate that our proposed approach has a great enhancement on overall classification accuracy compared with classical supervised algorithms and turns out to be a promising strategy in synergetic classification of HS and PAN images.