Hyperspectral image classification based on multiple kernel mutual learning

Abstract Multiple kernel learning (MKL) is a popular and effective method for hyperspectral image classification. However, the communication and interaction among multiple basic kernels are insufficient among multiple basic kernels during the whole training process for traditional MKL methods. In this paper, a multiple kernel learning framework based on self-learning and mutual learning (MKML) is proposed. First, each basic kernel starts pretraining with its own training samples and then uses a trained model to make predictions. Second, the basic kernel selects some informative unlabeled samples with high entropy and queries other basic kernels for labeling. All basic kernels except the one that raised the problem negotiate together to determine the class label of the unlabeled samples. Third, the new pseudo-labeled samples are added to the initial training sample sets to train the model again. Finally, all basic kernels are combined to obtain excellent classification performance by the voting mechanism. The methodology is validated on three real hyperspectral images. The experimental results show that the proposed method exhibits better classification performance than well-known MKL methods.