ADA-Net：基于注意力引导域自适应网络与对比学习的航空影像枯立木分割方法
ADA-Net: Attention-Guided Domain Adaptation Network with Contrastive Learning for Standing Dead Tree Segmentation Using Aerial Imagery
AI总结：
本文提出注意力引导域自适应网络 ADA-Net，结合增强对比学习，将无标注的美国航空影像转换到芬兰域，实现枯立木分割并取得最优性能。
摘要：
枯立木信息对于理解森林生态系统功能和恢复力十分重要，但在大地理区域上一直缺乏相关数据。气候变化已引发大规模树木死亡事件，由于数据有限，这些事件可能长期未被发现。在本研究中，我们提出一种利用航空多光谱正射影像分割枯立木的新方法。由于森林遥感中获取标注数据集一直是一个重要难题，需要森林专业知识，因此我们引入一种域迁移方法，利用域自适应学习从源域 X 到目标域 Y 的变换。在该图像到图像转换任务中，我们旨在通过预训练分割网络来利用目标域中可用的标注。当来自新研究区域且没有标注的图像被引入时（源域 X），这些图像会被转换到目标域。然后，通过在域自适应后的图像上推断预训练网络来应用迁移学习。除了研究当前域自适应方法在该目标上的可行性外，我们还提出一种新方法，称为注意力引导域自适应网络（ADA-Net），并增强了对比学习。相应地，ADA-Net 方法提供了新的最先进域自适应性能水平，优于现有方法。我们使用来自芬兰和美国的两个数据集对所提方法进行了评估。美国图像被转换到芬兰域，我们表明合成的 USA2Finland 数据集表现出与芬兰域图像相似的特征。软件实现共享于 https://github.com/meteahishali/ADA-Net。数据公开于 https://www.kaggle.com/datasets/meteahishali/aerial-imagery-for-standing-dead-tree-segmentation。
英文摘要：
Information on standing dead trees is important for understanding forest ecosystem functioning and resilience but has been lacking over large geographic regions. Climate change has caused large-scale tree mortality events that can remain undetected due to limited data. In this study, we propose a novel method for segmenting standing dead trees using aerial multispectral orthoimages. Because access to annotated datasets has been a significant problem in forest remote sensing due to the need for forest expertise, we introduce a method for domain transfer by leveraging domain adaptation to learn a transformation from a source domain X to target domain Y. In this Image-to-Image translation task, we aim to utilize available annotations in the target domain by pre-training a segmentation network. When images from a new study site without annotations are introduced (source domain X), these images are transformed into the target domain. Then, transfer learning is applied by inferring the pre-trained network on domain-adapted images. In addition to investigating the feasibility of current domain adaptation approaches for this objective, we propose a novel approach called the Attention-guided Domain Adaptation Network (ADA-Net) with enhanced contrastive learning. Accordingly, the ADA-Net approach provides new state-of-the-art domain adaptation performance levels outperforming existing approaches. We have evaluated the proposed approach using two datasets from Finland and the US. The USA images are converted to the Finland domain, and we show that the synthetic USA2Finland dataset exhibits similar characteristics to the Finland domain images. The software implementation is shared at https://github.com/meteahishali/ADA-Net. The data is publicly available at https://www.kaggle.com/datasets/meteahishali/aerial-imagery-for-standing-dead-tree-segmentation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
