Unsupervised Domain Adaptation With Debiased Contrastive Learning and Support-Set Guided Pseudolabeling for Remote Sensing Images

The variability in different altitudes, geographical variances, and weather conditions across datasets degrade state-of-the-art (SOTA) deep neural network object detection performance. Unsupervised and semisupervised domain adaptations (DAs) are decent solutions to bridge the gap between two different distributions of datasets. The SOTA pseudolabeling process is susceptible to background noise, hindering the optimal performance in target datasets. The existing contrastive DA methods overlook the bias effect introduced from the false negative (FN) target samples, which mislead the complete learning process. This article proposes support-guided debiased contrastive learning for DA to properly label the unlabeled target dataset and remove the bias toward target detection. We introduce: 1) a support-set curated approach to generate high-quality pseudolabels from the target dataset proposals; 2) a reduced distribution gap across different datasets using domain alignment on local, global, and instance-aware features for remote sensing datasets; and 3) novel debiased contrastive loss function that makes the model more robust for the variable appearance of a particular class over images and domains. The proposed debiased contrastive learning pivots on class probabilities to address the challenge of FNs in the unsupervised framework. Our model outperforms the compared SOTA models with a minimum gain of +3.9%, +3.2%, +12.7%, and +2.1% of mean average precision for DIOR, DOTA, Visdrone, and UAVDT datasets, respectively.