A novel semi-supervised hyperspectral image classification approach based on spatial neighborhood information and classifier combination

Abstract

In the process of semi-supervised hyperspectral image classification, spatial neighborhood information of training samples is widely applied to solve the small sample size problem. However, the neighborhood information of unlabeled samples is usually ignored. In this paper, we propose a new algorithm for hyperspectral image semi-supervised classification in which the spatial neighborhood information is combined with classifier to enhance the classification ability in determining the class label of the selected unlabeled samples. There are two key points in this algorithm: (1) it is considered that the correct label should appear in the spatial neighborhood of unlabeled samples; (2) the combination of classifier can obtains better results. Two classifiers multinomial logistic regression (MLR) and k-nearest neighbor (KNN) are combined together in the above way to further improve the performance. The performance of the proposed approach was assessed with two real hyperspectral data sets, and the obtained results indicate that the proposed approach is effective for hyperspectral classification.