In this paper, an adaptive semi-supervised rotation forest (SSRoF) algorithm is proposed for the classification of hyperspectral images with limited training data. Our proposition is based on Rotation Forest (RoF), a classifying technique that has proved to be remarkably accurate in the context of high-dimensional data. It is adapted to the semi-supervised context, by increasing the number of training instances in the learning stage, with high-quality unlabeled samples mined using ensemble margin. SMOTE is adopted to overcome the class imbalance problem. Out-Of-Bag (OOB) instances are used in a second phase to figure out the optimal number of samples to be added to the training set.

Five ensemble methods and five semi-supervised methods are employed as comparisons. The results on three real hyperspectral remote sensing datasets demonstrate the effectiveness of the proposed method.