arXiv 2509.058952026-01-27cs.CV

BTCChat: 通过多模态大语言模型推进遥感双时相变化描述

BTCChat: Advancing Remote Sensing Bi-temporal Change Captioning with Multimodal Large Language Model

State Key Laboratory of Networking and Switching Technology（网络与交换技术国家重点实验室）；Beijing University of Posts and Telecommunications（北京邮电大学）；Aerospace Information Research Institute（航天信息研究所）；Chinese Academy of Sciences（中国科学院）；School of Geographical Sciences（地理科学学院）；Hunan Normal University（湖南师范大学）

Yujie Li, Wenjia Xu, Yuanben Zhang, Zhiwei Wei, Mugen Peng

备注：: 5 pages, 2 figures; Accepted by ICASSP 2026

阅读 PDF

arXiv 页面 ↗HTML 原文 ↗

AI总结：

BTCChat通过多模态大语言模型提升遥感双时相变化描述能力，引入变化提取模块和提示增强机制，实现更精确的视觉-语义对齐和更优的性能表现。

摘要：

双时相卫星影像支持关键应用，如城市化监测和灾害评估。尽管强大的多模态大语言模型（MLLMs）已应用于双时相变化分析，但先前方法通过直接拼接图像对进行处理，无法充分建模时间相关性和空间语义变化。这种不足阻碍了视觉-语义对齐在变化理解中的应用，从而限制了当前方法的整体效果。为解决这一差距，我们提出了BTCChat，一种具有先进双时相变化理解能力的多时相MLLM。BTCChat支持双时相变化描述并保留单图像解释能力。为了更好地捕捉图像对中的时间特征和空间语义变化，我们设计了变化提取模块。此外，为了增强模型对空间细节的关注，我们引入了提示增强机制，该机制将上下文线索整合到提示中以提升模型性能。实验结果表明，BTCChat在变化描述和视觉问答任务上实现了最先进的性能。代码可在https://github.com/IntelliSensing/BTCChat处获取。

英文摘要：

Bi-temporal satellite imagery supports critical applications such as urbanization monitoring and disaster assessment. Although powerful multimodal large language models~(MLLMs) have been applied in bi-temporal change analysis, previous methods process image pairs through direct concatenation, inadequately modeling temporal correlations and spatial semantic changes. This deficiency hampers visual-semantic alignment in change understanding, thereby constraining the overall effectiveness of current approaches. To address this gap, we propose BTCChat, a multi-temporal MLLM with advanced bi-temporal change understanding capability. BTCChat supports bi-temporal change captioning and retains single-image interpretation capability. To better capture temporal features and spatial semantic changes in image pairs, we design a Change Extraction module. Moreover, to enhance the model's attention to spatial details, we introduce a Prompt Augmentation mechanism, which incorporates contextual clues into the prompt to enhance model performance. Experimental results demonstrate that BTCChat achieves state-of-the-art performance on change captioning and visual question answering tasks. The code is available \href{https://github.com/IntelliSensing/BTCChat}{here}.

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。