A simple and effective semi-supervised learning framework for hyperspectral image classification

Semisupervised learning (SSL) is often used when the number of labeled samples is very small compared to the number of unlabeled samples. It permits the exploitation of structure within unlabeled samples during the learning task. Like many other applications, remote sensing images suffer from the limited number of ground-truth samples and therefore semisupervised techniques may be used to overcome this limitation. In this paper, a semisupervised framework is proposed for classification of hyperspectral images with scarce labeled samples. Our method, which we call SSL-CC, utilizes fuzzy spectral clustering to label spatially neighboring samples. SSL-CC is implemented and tested on two benchmark hyperspectral datasets. Fuzzy clustering is compared to traditional crisp clustering (k-means) and the obtained results indicate that fuzzy clustering can significantly improve classification accuracy. SSL-CC achieves on average 60% improvement over a baseline SVM classifier.