Here, we address the challenge of generalizing object detection and labeling in remote sensing data from one dataset to another. It is the variability in different altitudes, geographical variances, weather conditions, and object size across datasets that state-of-the-art DNN largely fails to generalize. Contrastive-based unsupervised domain adaptation attempts to bridge the gap by producing discriminating features for frames and instances across different datasets, and we have shown some progress on the domain alignment on local, global, and instance-aware features for remote sensing data. In this research, we propose using support-guided pseudo-labeling on the target domain instances to enable instance domain adaptation on the source and target datasets. Next, we introduce the contrastive loss function with multiple positive examples to make the model more generalized of the variable appearance of a particular class over images and domains. Also, we proposed debiased contrastive learning based on class probabilities to address the challenge of false negatives in the unsupervised framework. We show the advantages of the proposed model on satellite (DIOR and DOTA2.0) and drone (Visdrone and UAVDT) image datasets.