MergeSAM: Unsupervised change detection of remote sensing images based on the Segment Anything Model
This work addresses the problem of detecting complex changes in remote sensing imagery for applications like environmental monitoring, though it is incremental as it builds on existing SAM capabilities.
The paper tackles unsupervised change detection in high-resolution remote sensing images by introducing MergeSAM, a method based on the Segment Anything Model (SAM), which uses MaskMatching and MaskSplitting strategies to handle complex changes like object splitting and merging, resulting in improved detection of intricate land cover changes.
Recently, large foundation models trained on vast datasets have demonstrated exceptional capabilities in feature extraction and general feature representation. The ongoing advancements in deep learning-driven large models have shown great promise in accelerating unsupervised change detection methods, thereby enhancing the practical applicability of change detection technologies. Building on this progress, this paper introduces MergeSAM, an innovative unsupervised change detection method for high-resolution remote sensing imagery, based on the Segment Anything Model (SAM). Two novel strategies, MaskMatching and MaskSplitting, are designed to address real-world complexities such as object splitting, merging, and other intricate changes. The proposed method fully leverages SAM's object segmentation capabilities to construct multitemporal masks that capture complex changes, embedding the spatial structure of land cover into the change detection process.