RFA-Net: Reconstructed Feature Alignment Network for Domain Adaptation Object Detection in Remote Sensing Imagery

With the development of deep learning, great progress has been made in object detection of remote sensing (RS) imagery. However, the object detector is hard to generalize well from one labeled dataset (source domain) to another unlabeled dataset (target domain) due to the discrepancy of data distribution (domain shift). Currently, adversarial-based domain adaptation methods align the semantic features of source and target domain features to alleviate the domain shift. But they fail to avoid the alignment of noisy background features and neglect the instance-level features, which are inappropriate for detection models that focus on instance location and classification. To mitigate domain shift existing in object detection, we propose a reconstructed feature alignment network (RFA-Net) for unsupervised cross-domain object detection in remote sensing imagery. The RFA-Net includes one sequential data augmentation module (SDA) deployed on data level for providing solid gains on unlabeled data, one sparse feature reconstruction module (SFR) deployed on feature level to intensify instance feature for feature alignment, and one pseudo-label generation module (PLG) deployed on label level for the supervision of the unlabeled target domain. Extensive experiments illustrate that our proposed RFA-Net is effective to alleviate the domain shift problem in domain adaptation object detection of RS imagery.