AdaTreeFormer: Few Shot Domain Adaptation for Tree Counting from a Single High-Resolution Image

• URL: http://arxiv.org/abs/2402.02956v4
• Date: Sun, 30 Jun 2024 09:08:55 GMT
• Title: AdaTreeFormer: Few Shot Domain Adaptation for Tree Counting from a Single High-Resolution Image
• Authors: Hamed Amini Amirkolaee, Miaojing Shi, Lianghua He, Mark Mulligan,
• Abstract summary: This paper proposes a framework that is learnt from the source domain with sufficient labeled trees. It is adapted to the target domain with only a limited number of labeled trees. Experimental results show that AdaTreeFormer significantly surpasses the state of the art.
• Score: 11.649568595318307
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. Experimental results show that AdaTreeFormer significantly surpasses the state of the art, \eg in the cross domain from the Yosemite to Jiangsu dataset, it achieves a reduction of 15.9 points in terms of the absolute counting errors and an increase of 10.8\% in the accuracy of the detected trees' locations. The codes and datasets are available at https://github.com/HAAClassic/AdaTreeFormer.

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