Tri-training for remote sensing classification based on multi-scale homogeneity

In the process of hyperspectral image classification, the number of training samples is the key problem in improvement of classification performance. However, finding training samples are generally difficult and time-consuming. In this paper, we propose a novel semi-supervised approach and attempt to utilize unlabeled samples to improve classification accuracy. Specifically, active learning (AL) and multi-scale homogeneity (MSH) are integrated in a tri-training framework, where unlabeled samples are selected using AL and the labels of unlabeled samples are predicted from rough classification results with consideration of spatial neighborhood information. The MSH method is utilized to process the classification results to generate the final classification results. Moreover, we propose a novel diversity measure to select optimal classifier combination from different classifiers including support vector machine (SVM), multinomial logistic regression (MLR), extreme learning machine (ELM), k-nearest neighbor (KNN), and random forest (RF) etc. Experiments on two real hyperspectral data indicate that the new diversity measure can select an optimal classifier combination, and the proposed approach can effectively improve classification performance.