## 6 Conclusions  
In this paper, we proposed an end-to-end DA detector with cascade RPNs (i.e., CASDA) to realize the cross-regional tree-crown detection and counting. To deal with the problems of a poor abundance of objects and the data imbalance in tree-crown detection, the cascade RPNs were designed to adopt multiple region proposal networks to filter out easy samples so that the learning ability of deeper classifiers was gradually enhanced. Then, the adaptation components and detector were integrated to form an end-to-end framework for the cross-regional detection. In addition, a practical filtering method was proposed according to the observed tree-crown distribution rules to effectively eliminate the wrongly detected trees. Experiments in two different adaptation scenarios showed that our method achieved 68.95% and 88.83% average F1-scores, respectively, significantly outperforming the other DA approaches focusing on diminishing the domain shift across common benchmarks, showing its effectiveness in cross-domain tree-crown detection using remote sensing images. Particularly, our method obtained a greater performance boost for the adaption with a larger domain shift. From the experimental results, we could draw the conclusion that, in tree-crown detection, it is more effective to improve the detection performance on the source domain than to diminish the domain shift, especially when confronted with a significant domain shift.  
Moreover, our method has the potential to realize other DA object detection with similar characteristics to overcome the scarcity of labeled data and the difficulty of traditional deep learning methods in transfer learning across different domains. For example, in the remote sensing field, it can be directly applied to the cross-regional growing-status observation of the same tree species after labeling the source domain with fine-grained labels. Furthermore, fault detection in different environments, such as the fault detection of mechanical equipment, high-voltage line, etc., is also a possible scenario, where the forms of the fault are few and the image background is very complex, limiting the performance  
Figure 10: Response maps in different RPN stages. RPN is sequentially more discriminative, and the tree crowns are localized more accurately.  
of traditional RPN-based detector. Introducing DA methods with cascade RPNs could be beneficial by saving labeling costs and locating the fault more precisely.  
Nevertheless, there is still much room for improving the performance of cross-domain detection between satellite images and UAV images due to the great difference in styles, textures, etc. Therefore, we hope to build a more effective cross-domain tree-crown detector for this adaptation in the future.  
Methodology, Y.W. and G.Y.; writing--original draft preparation, Y.W.; writing--review and editing, G.Y. and H.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Key Research and Development Program of China (grant no. 2020 YFE0200800) and the National Natural Science Foundation of China (grant no. 42001376).  
Not applicable.  
The authors declare no conflict of interest.