Batch Mode Active Learning for Geographical Image Classification

In this paper, an innovative batch mode active learning by combining discriminative and representative information for hyperspectral image classification with support vector machine is proposed. In the past years, the batch mode active learning mainly exploits different query functions, which are based on two criteria: uncertainty criterion and diversity criterion. Generally, the uncertainty criterion and diversity criterion are independent of each other, and they also could not make sure the queried samples identical and independent distribution. In the proposed method, the diversity criterion is focused. In the innovative diversity criterion, firstly, we derive a novel form of upper bound for true risk in the active learning setting, by minimizing this upper bound to measure the discriminative information, which is connected with the uncertainty. Secondly, for the representative information, the maximum mean discrepancy(MMD) which captures the representative information of the data structure is adopt to match the distribution of the labeled samples and query samples, to make sure the queried samples have a similar distribution to the labeled samples and guarantee the queried samples are diversified. Meanwhile, the number of new queried samples is adaptive, which depends on the distribution of the labeled samples. In the experiment, we employ two benchmark remote sensing images, Indian Pines and Washington DC. The experimental results demonstrate the effective of our proposed method compared with the state-of-the-art AL methods