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

Ching-Heng Cheng; Chih-Chung Hsu

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arXiv · 2511.16322

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

Ching-Heng Cheng·Chih-Chung Hsu

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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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BibTeX RIS

Ching-Heng Cheng, Chih-Chung Hsu. 2025-11-20. ChangeDINO: DINOv3-Driven Building Change Detection in Optical Remote Sensing Imagery. https://arxiv.org/abs/2511.16322

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