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Combining rotation forests and adaboost for hyperspectral imagery classification using few labeled samples

Classification of hyperspectral imagery using too few labeled samples is a challenging problem considering the high dimensionality of hyperspectral imagery. In this paper, an ensemble method combining rotation forests and AdaBoost is proposed to tackle this problem. By adaptive boosting, AdaBoost can significantly reduce classification error in an iteration compared to a single classifier, and the rotation matrix can increase diversity so that the ensemble performance can be further improved. Experimental resutls show that the final classification accuracy of the proposed algorithm consistently outperforms other state-of-the-art classification methods.