DFENet for Domain Adaptation based Remote Sensing Scene Classification

Domain adaptation scene classification refers to the task of scene classification where the training set (called source domain) has different distributions from the test set (called target domain). Although remarkable results have been reported, the misalignment of source and target domain features still remains as a big challenge when the large intra-class variances of remote sensing images encounter the insufficient exploration of discriminative feature representations for both domains. To address this challenge, a novel domain feature enhancement network is proposed to adaptively enhance the discriminative ability of the learned features for dealing with the domain variances of scene classification. Specifically, an adaptive context-aware feature refinement module is first designed to automatically recalibrate global and local features by explicitly modeling interdependencies between the channel and spatial for each domain. Then, a multi-level adversarial dropout module is further designed to strengthen the generalization capability of our network by adaptively reconfiguring the sparsity of the feature level and decision level in the target domain. The cooperation of context-aware feature refinement module and multi-level adversarial dropout module formulates a unique domain feature enhancement network that can be learned in an end-to-end manner. Comprehensive experiments show that our proposed method is better than state-of-the-art methods on Merced→RSSCN7, AID→RSSCN7, NWPU → RSSCN7, RSSCN7 → Merced, RSSCN7 → AID, and RSSCN7→NWPU datasets.