Attention-Based Dynamic Alignment and Dynamic Distribution Adaptation for Remote Sensing Cross-Domain Scene Classification
Cong Yang, Yanni Dong, Bo Du, Liangpei Zhang
Abstract
Due to the lack of high-quality labeled data and poor generalization ability of supervised models in remote sensing scene classification, cross-domain scene classification is proposed to better utilize the existing knowledge to classify the unlabeled data. Since there is a data distribution difference between the training (source domain) and test (target domain) set, many deep domain adaptation methods have been proposed to reduce such distribution discrepancy. However, existing deep domain adaptation methods usually use the discrepancy metric function to align the marginal distribution and do not consider the effect of each sample in different domains on the network weights. In addition, the existing domain adaptation methods cannot adaptively balance the relative importance of marginal and conditional distributions well. To overcome the above shortcomings, we propose a novel Attention-based Dynamic Alignment and Dynamic Distribution Adaptation (ADA-DDA) method to better align the marginal distribution between different distributions by calculating the dynamic weights of each sample in different domains and dynamically balance the relative importance of marginal and conditional distributions. Moreover, the attention mechanism enables purposeful knowledge transfer, so that the extracted features can be highly discriminative. The experimental results demonstrate that our proposed method is superior to the other state-of-the-art deep domain adaptation methods in the comparison, and outperforms the second place in accuracy by 5.36%.