DualDA-Net: Dual-Head Rectification for Cross-Domain Object Detection of Remote Sensing

Object detectors of remote sensing (RS) imagery with deep learning have become increasingly popular and rely heavily on extensive labeled data. The source-only detectors, which are trained on massive labeled data in a source domain, in some cases fail to get satisfactory performance on a target domain due to the domain shift. To alleviate the domain shift, popular approaches consider feature distribution alignment, but the target domain with massive unlabeled data is underutilized. Some methods use the source-only model to generate pseudo-labels for target domain data, but the variation of different RS scenarios produces the domain shift, which is injected directly into the pseudo-labels. Therefore, we propose the dual-head rectification domain adaptation network (DualDA-Net) to alleviate the domain shift and exploit the potential of unlabeled target domain data. DualDA-Net cooperates the coarse-to-fine consistency alignment (CCA) with dual-head co-training (DHCT) to align the distribution and generate pseudo-labels progressively. Specifically, the CCA focuses on source and target domain feature distribution alignment via CCA on multilevel features. Moreover, the DHCT with dual detection heads is deployed in the teacher–student framework, where one of the heads complements the other with high-quality predictions to rectify the pseudo-labels as supervision and alleviate the biased information. Sufficient experiments have been conducted on several domain adaptation settings. The experimental results demonstrate that our DualDA-Net achieves success in the target domain for cross-domain object detection of RS imagery.