Graph-based semi-supervised learning with GPU on small sample sized hyperspectral images

In hyperspectral images, the creation of ground-truth data for supervised learning methods is costly in terms of computation cost and time. In addition, the number of labeled data and the quality of labeled training data affects the success of the classification. as a solution to this problem, a graph-based semi-supervised hyperspectral image classifier is proposed in this study. The system was developed on graphics processing unit (GPU) to get rid of the high processing cost of semi-supervised learning. In addition, subtractive clustering is proposed as a new approach to select labeled samples for semi-supervised learning. The results of the system tests with public data sets showed that the classification performance of semi-supervised learning can be close to supervised learning with a small number of labeled data.