The process of estimating and counting tree density using only a single aerial or satellite image is a difficult task in the fields of photogrammetry and remote sensing. However, it plays a crucial role in the management of forests. The huge variety of trees in varied topography severely hinders tree counting models to perform well. The purpose of this paper is to propose a framework that is learnt from the source domain with sufficient labeled trees and is adapted to the target domain with only a limited number of labeled trees. Our method, termed as AdaTreeFormer, contains one shared encoder with a hierarchical feature extraction scheme to extract robust features from the source and target domains. It also consists of three subnets: two for extracting self-domain attention maps from source and target domains respectively and one for extracting cross-domain attention maps. For the latter, an attention-to-adapt mechanism is introduced to distill relevant information from different domains while generating tree density maps; a hierarchical cross-domain feature alignment scheme is proposed that progressively aligns the features from the source and target domains. We also adopt adversarial learning into the framework to further reduce the gap between source and target domains. Our AdaTreeFormer is evaluated on six designed domain adaptation tasks using three tree counting datasets, ie Jiangsu, Yosemite, and London; and outperforms the state of the art methods significantly.

翻译：利用单张航空或卫星图像估算并计数树木密度的过程在摄影测量与遥感领域是一项困难任务，然而它在森林管理中起着关键作用。不同地形中树木的巨大多样性严重阻碍了树木计数模型的性能表现。本文旨在提出一个框架，该框架从具有充足标记树木的源域学习，并适应仅含有限标记树木的目标域。我们的方法命名为AdaTreeFormer，包含一个带有层次化特征提取方案的共享编码器，用于从源域和目标域提取鲁棒特征。此外，它由三个子网络组成：两个分别用于从源域和目标域提取自域注意力图，另一个用于提取跨域注意力图。针对后者，我们引入一种注意力到自适应机制，在生成树木密度图的同时从不同域中提取相关信息；并提出一种层次化跨域特征对齐方案，逐步对齐源域和目标域的特征。我们还采用对抗学习框架以进一步缩小源域与目标域之间的差距。我们的AdaTreeFormer在六个设计的域自适应任务上，使用三个树木计数数据集（即江苏、约塞米蒂和伦敦）进行评估，并显著优于现有最先进方法。