###### Abstract  
Rapid and accurate tree-crown detection is significant to forestry management and precision forestry. In the past few decades, the development and maturity of remote sensing technology has created more convenience for tree-crown detection and planting management. However, the variability of the data source leads to significant differences between feature distributions, bringing great challenges for traditional deep-learning-based methods on cross-regional detection. Moreover, compared with other tasks, tree-crown detection has the problems of a poor abundance of objects, an overwhelming number of easy samples and the existence of a quantity of impervious background similar to the tree crown, which make it difficult for the classifier to learn discriminative features. To solve these problems, we apply domain adaptation (DA) to tree-crown detection and propose a DA cascade tree-crown detection framework with multiple region proposal networks, dubbed CAS-DA, realizing cross-regional tree-crown detection and counting from multiple-source remote sensing images. The essence of the multiple region proposal networks in CAS-DA is obtaining the multilevel features and enhancing deeper label classifiers gradually by filtering simple samples of source domain at an early stage. Then, the cascade structure is integrated with a DA object detector and the end-to-end training is realized through the proposed cascade loss function. Moreover, a filtering strategy based on the planting rules of tree crowns is designed and applied to filter wrongly detected trees by CAS-DA. We verify the effectiveness of our method in two different domain shift scenarios, including adaptation between satellite and drone images and cross-satellite adaptation. The results show that, compared to the existing DA methods, our method achieves the best average F1-score in all adaptions. It is also found that the performance between satellite and drone images is significantly worse than that between different satellite images, with average F1-scores of 68.95% and 88.83%, respectively. Nevertheless, there is an improvement of 11.88%-40.00% in the former, which is greater than 0.50%-5.02% in the latter. The above results prove that in tree-crown detection, it is more effective for the DA detector to improve the detection performance on the source domain than to diminish the domain shift alone, especially when a large domain shift exists. domain adaptation; tree-crown detection; cascade RPNs; filtering strategy +
Footnote †: journal: _Algorithms_  
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in different regions with different sensors in practice, resulting in a distribution diversity in the feature space, i.e., a domain shift. In this case, the performance of traditional deep-learning-based methods will degrade dramatically if they are applied to images taken in different conditions directly. The most straightforward approach is to manually annotate the data from the target (new) domain, but it is expensive, labor-intensive, and there may not always be enough training data. Domain Adaptation (DA) is a transfer learning paradigm that aligns the data distribution of the source domain and the target domain by learning new feature representations, so the model trained on the labeled source domain can be transferred to the target domain that is completely unlabeled or contains a few labeled data, without significant performance loss [5]. According to the visibility and quality of data labels on the source and target domains, DA can be classified into supervised DA (SDA), semi-supervised DA (SSDA), and unsupervised DA (UDA) [6]. Our proposed method concentrates on the scenario in which there is a massive quantity of clean data in the source domain and the labels of the target data are totally unobtainable, i.e., UDA. However, most of the existing DA models are proposed and optimized on detection benchmarks, which contain various categories of objects.