基于SAM的建筑变化检测：结合分布感知傅里叶适配与边缘约束变形
SAM-Based Building Change Detection with Distribution-Aware Fourier Adaptation and Edge-Constrained Warping
AI总结：
针对SAM在建筑变化检测中因域差距、分布不平衡和时相错位导致的性能问题，提出FAEWNet，通过分布感知傅里叶适配和边缘约束变形提升检测精度与边缘识别能力，在三个数据集上取得最优结果。
摘要：
建筑变化检测在城市发展、灾害评估和军事侦察中仍具挑战性。尽管Segment Anything Model（SAM）等基础模型展现出强大的分割能力，但由于域差距问题，SAM在建筑变化检测任务中存在局限性。现有基于适配器的微调方法面临建筑分布不平衡的挑战，导致细微变化检测不佳和边缘提取不准确。此外，变化检测中的双时相错位通常通过光流解决，但仍易受背景噪声影响，这会影响建筑变化检测，降低检测精度和边缘识别能力。为应对这些挑战，我们提出一种新的基于SAM的网络，即结合分布感知傅里叶适配与边缘约束变形的FAEWNet用于建筑变化检测。FAEWNet利用SAM编码器从遥感图像中提取丰富的视觉特征。为引导SAM聚焦遥感场景中的特定地物，我们提出分布感知傅里叶聚合适配器来聚合面向任务的变化信息。该适配器不仅有效解决域差距问题，还关注变化建筑的分布。此外，为减轻噪声干扰和高度偏移估计中的错位，我们设计了一种新型流模块，以优化建筑边缘提取并增强对变化建筑的感知。我们在LEVIR-CD、S2Looking和WHU-CD数据集上的最新结果证明了FAEWNet的有效性。代码可在https://github.com/SUPERMAN123000/FAEWNet获取。
英文摘要：
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 https://github.com/SUPERMAN123000/FAEWNet.
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。
