## 2 Related Works
### Unsupervised Domain-Adaptive Object Detection  
Turning to the case where the annotation in the source domain is available, while the target domain is not, the UDA methods are also applied to object detection tasks as an effective solution to the domain shift.  
Currently, compared with UDA image classification [33; 34] and UDA semantic segmentation [35; 36], UDA object detection is still at a relatively early stage. According to [6], deep DA methods can be summarized into three types: discrepancy-based DA, adversarial-based DA, and reconstruction-based DA. Discrepancy-based DA methods fine-tune the basic network trained with source data using a discrepancy-based loss. [PERSON] et al. [37] first realized DA object detection with maximum mean discrepancy(MMD), which regarded the mean value of positions as a representation of distribution. Considering MMD ignored the variance representing the distribution scale, [PERSON] et al. [38] proposed a center-based transfer feature learning method to reduce distribution differences. In adversarial-based DA, domain-invariant features can be obtained through adversarial learning. In adversarial-based DA methods, Faster R-CNN is widely used as baseline due to its high flexibility and extensibility in realizing an end-to-end training. Among these methods, DA Faster R-CNN [39] innovatively tackled the domain shift at the image level and instance level with adversarial learning and integrated these components with Faster R-CNN, realizing the end-to-end training. Later, [PERSON] et al. [40] proposed a Strong Weak Faster R-CNN containing a strong local alignment and a weak global alignment, further promoting the performance of the detection. In addition, [PERSON] et al. [41] proposed two regularization components to assist the above domain-adaptive Faster R-CNN series to focus on crucial image regions and instances. The reconstruction-based DA method improves the detection performance by reconstructing the source domain or target domain [42; 43]. Although the above methods make significant progresses in DA object detection on common benchmarks (i.e., PASCAL VOC), how to make the corresponding optimization for tree detection in complex scenarios and poor data abundance is a key problem that needs to be solved.  
In the remote sensing field, differences in atmospheric and ground conditions can easily disrupt the generalization ability of deep learning models, and DA is applied to process multitemporal and multisource satellite images to realize computational vision tasks, such as image classification and scene classification, which provide an effective way to observe climate change and the impact of human activities. Comparatively, studies in DA object detection using remote sensing images are relatively weak. We noticed that [PERSON] et al. [44] first applied the correlation alignment and adversarial-based DA methods to satellite-image vehicle detection and proposed a reconstruction loss to assist the model in learning semantic features more effectively, boosting the detection accuracy in the target domain by more than 10%.  
In tree-crown detection, CROPTD [45] first realized the cross-regional oil palm detection from two regions using a two-stage DA strategy. Considering that not all local features had the same transferability, it guided the model to focus on transferable local feature regions with a local attention mechanism. Moreover, [PERSON] et al. [46] proposed a multilevel attention DA network and an intersection over union (IoU)-based method to merge similar bounding boxes and obtain more accurate results. These DA tree-crown detection methods are mainly bridging the gap in the feature distribution with adversarial learning and seeking a further domain alignment with an attention mechanism, but they overlook the poor learning and classification capacity on the source domain caused by the characteristics of the tree-crown detection scenario, which have a negative impact on the performance of DA object detectors.