Abstract
Building change detection remains challenging for urban development, disaster assessment, and military reconnaissance. While foundation models like Segment Anything Model (SAM) show strong segmentation capabilities, SAM is limited in the task of building change detection due to domain gap issues. Existing adapter-based fine-tuning approaches face challenges with imbalanced building distribution, resulting in poor detection of subtle changes and inaccurate edge extraction. Additionally, bi-temporal misalignment in change detection, typically addressed by optical flow, remains vulnerable to background noises. This affects the detection of building changes and compromises both detection accuracy and edge recognition. To tackle these challenges, we propose a new SAM-Based Network with Distribution-Aware Fourier Adaptation and Edge-Constrained Warping (FAEWNet) for building change detection. FAEWNet utilizes the SAM encoder to extract rich visual features from remote sensing images. To guide SAM in focusing on specific ground objects in remote sensing scenes, we propose a Distribution-Aware Fourier Aggregated Adapter to aggregate task-oriented changed information. This adapter not only effectively addresses the domain gap issue, but also pays attention to the distribution of changed buildings. Furthermore, to mitigate noise interference and misalignment in height offset estimation, we design a novel flow module that refines building edge extraction and enhances the perception of changed buildings. Our state-of-the-art results on the LEVIR-CD, S2Looking and WHU-CD datasets highlight the effectiveness of FAEWNet. The code is available at this https URL.
Abstract (translated)
构建变化检测在城市发展、灾害评估和军事侦察中仍面临挑战。尽管像Segment Anything Model (SAM)这样的基础模型显示出了强大的分割能力，但SAM在建筑变化检测任务上因领域差距问题而受到限制。现有的基于适配器的微调方法面对不平衡的建筑物分布时也遇到了挑战，导致细微变化难以被发现，并且边缘提取不够准确。此外，在使用光学流处理双时间序列错位的变化检测中，背景噪声对其构成了威胁，影响了建筑变化的检测并削弱了检测精度和边缘识别。 为了解决这些问题，我们提出了一种新的基于SAM的网络——带有分布感知傅里叶适应和边界约束扭曲（FAEWNet）的建筑物变化检测方法。该网络利用SAM编码器从遥感图像中提取丰富的视觉特征。为了引导SAM关注特定地面目标，我们提出了一个分布感知傅里叶聚合适配器来整合任务导向的变化信息。这个适配器不仅有效地解决了领域差距问题，还注意到了变化建筑的分布情况。此外，为了解决噪声干扰和高度偏移估计中的错位问题，我们设计了一个新的流模块以精化建筑物边缘提取，并增强了对变化建筑物的感知。 在LEVIR-CD、S2Looking以及WHU-CD数据集上的最先进的实验结果证明了FAEWNet的有效性。该代码可在以下链接获取：[这里提供原网址]。
URL
https://arxiv.org/abs/2504.12619
