Discover Changes Gradually: LLM-Guided Progressive Network for Remote Sensing Change Detection

Existing remote sensing change detection (RSCD) method usually adopts one-shot prediction strategy, which is vulnerable to complex background and illumination variation, resulting in missed or false detection especially around ambiguous change boundary. Human visual perception follows a coarse-to-fine analysis process, progressively focusing from salient region to fine-grained detail. Inspired by this process, this paper formulates RSCD as a gradual reasoning process that progressively discovers change region. First, task-oriented question prompt is designed to guide large language model (LLM) to generate high-level semantic prior from bi-temporal image, including change region, boundary characteristic, and potential missed or false detection, etc. Additionally, shared dictionary–coefficient representation learning is utilized to project bi-temporal image into unified representation space, where coefficient-level visual difference information is extracted. Furthermore, text–image consistency is explored to transform global semantic prior into spatial-aware response, thereby adaptively enhancing visual difference feature.Subsequently, Segment Anything Model (SAM) is employed to achieve progressive change discovery strategy. It jointly considers change probability, prediction reliability, and remaining search region to generate multi-stage high-quality prompt, guiding SAM to progressively focus on unresolved change region. Moreover, the soft-union fusion is adopted to obtain final change result. Experiments on multiple datasets demonstrate that the proposed model achieves superior performance and exhibits stronger effectiveness in complex change identification.