DeltaVLM: Interactive Remote Sensing Image Change Analysis via Instruction-guided Difference Perception

• URL: http://arxiv.org/abs/2507.22346v1
• Date: Wed, 30 Jul 2025 03:14:27 GMT
• Title: DeltaVLM: Interactive Remote Sensing Image Change Analysis via Instruction-guided Difference Perception
• Authors: Pei Deng, Wenqian Zhou, Hanlin Wu,
• Abstract summary: We introduce remote sensing image change analysis (RSICA) as a new paradigm that combines the strengths of change detection and visual question answering.<n>We propose DeltaVLM, an end-to-end architecture tailored for interactive RSICA.<n>DeltaVLM features three innovations: (1) a fine-tuned bi-temporal vision encoder to capture temporal differences; (2) a visual difference perception module with a cross-semantic relation measuring mechanism to interpret changes; and (3) an instruction-guided Q-former to effectively extract query-relevant difference information.
• Score: 0.846600473226587
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate interpretation of land-cover changes in multi-temporal satellite imagery is critical for real-world scenarios. However, existing methods typically provide only one-shot change masks or static captions, limiting their ability to support interactive, query-driven analysis. In this work, we introduce remote sensing image change analysis (RSICA) as a new paradigm that combines the strengths of change detection and visual question answering to enable multi-turn, instruction-guided exploration of changes in bi-temporal remote sensing images. To support this task, we construct ChangeChat-105k, a large-scale instruction-following dataset, generated through a hybrid rule-based and GPT-assisted process, covering six interaction types: change captioning, classification, quantification, localization, open-ended question answering, and multi-turn dialogues. Building on this dataset, we propose DeltaVLM, an end-to-end architecture tailored for interactive RSICA. DeltaVLM features three innovations: (1) a fine-tuned bi-temporal vision encoder to capture temporal differences; (2) a visual difference perception module with a cross-semantic relation measuring (CSRM) mechanism to interpret changes; and (3) an instruction-guided Q-former to effectively extract query-relevant difference information from visual changes, aligning them with textual instructions. We train DeltaVLM on ChangeChat-105k using a frozen large language model, adapting only the vision and alignment modules to optimize efficiency. Extensive experiments and ablation studies demonstrate that DeltaVLM achieves state-of-the-art performance on both single-turn captioning and multi-turn interactive change analysis, outperforming existing multimodal large language models and remote sensing vision-language models. Code, dataset and pre-trained weights are available at https://github.com/hanlinwu/DeltaVLM.

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