Change Detection Between Optical Remote Sensing Imagery and Map Data via Segment Anything Model (SAM)

• URL: http://arxiv.org/abs/2401.09019v1
• Date: Wed, 17 Jan 2024 07:30:52 GMT
• Title: Change Detection Between Optical Remote Sensing Imagery and Map Data via Segment Anything Model (SAM)
• Authors: Hongruixuan Chen and Jian Song and Naoto Yokoya
• Abstract summary: We explore unsupervised multimodal change detection between two key remote sensing data sources: optical high-resolution imagery and OpenStreetMap (OSM) data. We introduce two strategies for guiding SAM's segmentation process: the 'no-prompt' and 'box/mask prompt' methods. Experimental results on three datasets indicate that the proposed approach can achieve more competitive results.
• Score: 20.985372561774415
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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