Monitoring changes in the Earth's surface is crucial for understanding natural processes and human impacts, necessitating precise and comprehensive interpretation methodologies. Remote sensing satellite imagery offers a unique perspective for monitoring these changes, leading to the emergence of remote sensing image change interpretation (RSICI) as a significant research focus. Current RSICI technology encompasses change detection and change captioning, each with its limitations in providing comprehensive interpretation. To address this, we propose an interactive Change-Agent which integrates a multi-level change interpretation (MCI) model as eyes and a large language model (LLM) as the brain. Our Change-Agent can follow user instructions to achieve comprehensive change interpretation and insightful analysis according to user instructions, such as change detection and change captioning, change object counting, change cause analysis, etc. Our proposed MCI model contains two branches of pixel-level change detection and semantic-level change captioning, in which multiple BI-temporal Iterative Interaction (BI3) layers utilize Local Perception Enhancement (LPE) and the Global Difference Fusion Attention (GDFA) modules to enhance the model's discriminative feature representation capabilities. To train the MCI model, we build the LEVIR-MCI dataset with change masks and captions of bi-temporal images. Extensive experiments demonstrate the effectiveness of the proposed change interpretation model and highlight the promising potential of our Change-Agent in facilitating comprehensive and intelligent interpretation of surface changes. We will make our dataset and codebase of the change interpretation model and Change-Agent publicly available to facilitate future research at https://github.com/Chen-Yang-Liu/Change-Agent

通过改变检测和改变标题、改变对象计数和改变原因分析等用户指令实现全面的改变解释和深入分析的交互式改变代理及其多层次改变解释模型的提出，利用BI3和LPE、GDFA来增强模型的特征表示能力，并通过LEVIR-MCI数据集的训练和广泛实验证明了其有效性和潜在潜力，为未来的研究提供了数据和代码。

变革代理：从变化检测和变化字幕逐步推进互动综合变革解释与分析