Change-Agent：面向交互式综合遥感变化解译与分析
Change-Agent: Towards Interactive Comprehensive Remote Sensing Change Interpretation and Analysis
AI总结：
提出交互式Change-Agent，结合多层级变化解译模型与大型语言模型，实现遵循用户指令的全面遥感变化解译与分析，达到最先进性能。
摘要：
监测地球表面的变化对于理解自然过程和人类影响至关重要，需要精确且全面的解译方法。遥感卫星影像为监测这些变化提供了独特视角，促使遥感影像变化解译（RSICI）成为一个重要的研究焦点。当前的RSICI技术涵盖变化检测和变化描述，各自在提供全面解译方面存在局限性。为解决这一问题，我们提出了一种交互式Change-Agent，它能够遵循用户指令实现全面的变化解译和深入分析，例如变化检测与变化描述、变化目标计数、变化原因分析等。Change-Agent集成了一个多层级变化解译（MCI）模型作为“眼睛”，以及一个大语言模型（LLM）作为“大脑”。MCI模型包含像素级变化检测和语义级变化描述两个分支，其中提出了双时相迭代交互（BI3）层以增强模型的判别性特征表示能力。为支持MCI模型的训练，我们构建了LEVIR-MCI数据集，包含大量变化掩膜和变化描述。实验表明，MCI模型在同时实现变化检测和变化描述方面达到了最先进的性能，并突显了我们的Change-Agent在促进地表变化全面解译方面的应用价值，为智能遥感应用开辟了新途径。为促进未来研究，我们将在https://github.com/Chen-Yang-Liu/Change-Agent公开我们的数据集以及MCI模型和Change-Agent的代码库。
英文摘要：
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 can follow user instructions to achieve comprehensive change interpretation and insightful analysis, such as change detection and change captioning, change object counting, change cause analysis, etc. The Change-Agent integrates a multi-level change interpretation (MCI) model as the eyes and a large language model (LLM) as the brain. The MCI model contains two branches of pixel-level change detection and semantic-level change captioning, in which the BI-temporal Iterative Interaction (BI3) layer is proposed to enhance the model's discriminative feature representation capabilities. To support the training of the MCI model, we build the LEVIR-MCI dataset with a large number of change masks and captions of changes. Experiments demonstrate the SOTA performance of the MCI model in achieving both change detection and change description simultaneously, and highlight the promising application value of our Change-Agent in facilitating comprehensive interpretation of surface changes, which opens up a new avenue for intelligent remote sensing applications. To facilitate future research, we will make our dataset and codebase of the MCI model and Change-Agent publicly available at https://github.com/Chen-Yang-Liu/Change-Agent
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
