Unsupervised multimodal change detection is pivotal for time-sensitive tasks
and comprehensive multi-temporal Earth monitoring. In this study, we explore
unsupervised multimodal change detection between two key remote sensing data
sources: optical high-resolution imagery and OpenStreetMap (OSM) data.
Specifically, we propose to utilize the vision foundation model Segmentation
Anything Model (SAM), for addressing our task. Leveraging SAM's exceptional
zero-shot transfer capability, high-quality segmentation maps of optical images
can be obtained. Thus, we can directly compare these two heterogeneous data
forms in the so-called segmentation domain. We then introduce two strategies
for guiding SAM's segmentation process: the 'no-prompt' and 'box/mask prompt'
methods. The two strategies are designed to detect land-cover changes in
general scenarios and to identify new land-cover objects within existing
backgrounds, respectively. Experimental results on three datasets indicate that
the proposed approach can achieve more competitive results compared to
representative unsupervised multimodal change detection methods.

本研究探讨了无监督多模态变化检测在时效性任务和综合多时序地球监测中的重要性，并介绍了利用光学高分辨率图像和 OpenStreetMap（OSM）数据进行无监督多模态变化检测的方法。我们通过使用视觉基础模型 Segmentation Anything Model（SAM）来解决这一任务，利用 SAM 的优秀的零样本迁移能力获得高质量的光学图像分割图，从而能够直接比较这两种异构数据形态。我们引入了两种策略来指导 SAM 的分割过程：'no-prompt' 方法和 'box/mask prompt' 方法。这两种策略分别用于检测一般情况下的土地覆盖变化和在已有背景下识别新的土地覆盖对象。在三个数据集上的实验结果表明，所提出的方法与代表性的无监督多模态变化检测方法相比能够取得更具竞争力的结果。