ChangeDINO: DINOv3-Driven Building Change Detection in Optical Remote Sensing Imagery

• URL: http://arxiv.org/abs/2511.16322v1
• Date: Thu, 20 Nov 2025 12:55:13 GMT
• Title: ChangeDINO: DINOv3-Driven Building Change Detection in Optical Remote Sensing Imagery
• Authors: Ching-Heng Cheng, Chih-Chung Hsu,
• Abstract summary: This article introduces ChangeDINO, an end-to-end multiscale Siamese framework for optical building change detection.<n>The model fuses a lightweight backbone stream with features transferred from a frozen DINOv3.<n>Experiments on four public benchmarks show that ChangeDINO consistently outperforms recent state-of-the-art methods in IoU and F1.
• Score: 8.49298841726789
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Remote sensing change detection (RSCD) aims to identify surface changes from co-registered bi-temporal images. However, many deep learning-based RSCD methods rely solely on change-map annotations and underuse the semantic information in non-changing regions, which limits robustness under illumination variation, off-nadir views, and scarce labels. This article introduces ChangeDINO, an end-to-end multiscale Siamese framework for optical building change detection. The model fuses a lightweight backbone stream with features transferred from a frozen DINOv3, yielding semantic- and context-rich pyramids even on small datasets. A spatial-spectral differential transformer decoder then exploits multi-scale absolute differences as change priors to highlight true building changes and suppress irrelevant responses. Finally, a learnable morphology module refines the upsampled logits to recover clean boundaries. Experiments on four public benchmarks show that ChangeDINO consistently outperforms recent state-of-the-art methods in IoU and F1, and ablation studies confirm the effectiveness of each component. The source code is available at https://github.com/chingheng0808/ChangeDINO.

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