Article(id=1322961736998978527, tenantId=1146029695717560320, journalId=1249024232475115590, issueId=1322961355078246774, articleNumber=null, orderNo=null, doi=10.11834/jig.260217, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1776268800000, receivedDateStr=2026-04-16, revisedDate=1780329600000, revisedDateStr=2026-06-02, acceptedDate=null, acceptedDateStr=null, onlineDate=1793348257540, onlineDateStr=2026-10-30, pubDate=1789488000000, pubDateStr=2026-09-16, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793348257540, onlineIssueDateStr=2026-10-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793348257540, creator=13701087609, updateTime=1793348257540, updator=13701087609, issue=Issue{id=1322961355078246774, tenantId=1146029695717560320, journalId=1249024232475115590, year='2026', volume='31', issue='9', pageStart='3075', pageEnd='3440', issueExtLink='null', onlineDate='null', pubDate='1789488000000', pubDateStr='2026-09-16', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793348166484, creator='13701087609', updateTime=1793348617535, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322963246994555810, tenantId=1146029695717560320, journalId=1249024232475115590, issueId=1322961355078246774, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322963246998750115, tenantId=1146029695717560320, journalId=1249024232475115590, issueId=1322961355078246774, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=3251, endPage=3265, ext={EN=ArticleExt(id=1322961737858810848, articleId=1322961736998978527, tenantId=1146029695717560320, journalId=1249024232475115590, language=EN, title=Progress and perspectives on large models for aerial remote sensing intelligent interpretation, columnId=1322961355891941751, journalTitle=Journal of Image and Graphics, columnName=The intelligent empowerment of large models in the aviation field, runingTitle=null, highlight=null, articleAbstract=

With the proliferation of multisource sensors and the rapid progress of multimodal data fusion and intelligent interpretation techniques，aerial remote sensing continues to evolve from traditional single-modal perceptions toward multimodal perception and understanding. This progress demonstrates the significant potential for intelligent monitoring and decision-making in precision agriculture，urban environmental monitoring，ecological protection，and natural disaster assessment. Due to ongoing advancements in Earth observation capabilities，multisource remote sensing data，including optical imagery，synthetic aperture radar （SAR），and hyperspectral imagery are being increasingly characterized by high spatial resolution，multitemporal coverage，and multidimensional richness. These developments，in turn，offering a solid foundation for fine-grained land-cover identification and dynamic change analysis. Nevertheless，traditional remote sensing interpretation methods，which rely on single-source data and single-task learning models，struggle to cope with the complexities of real-world scenarios in which object scales vary dramatically，semantic layers are rich and entangled，and spatiotemporal heterogeneity is strong. Because such methods cannot adequately balance holistic scene semantics with local fine-grained details，this limits their capacity for high-level semantic understanding and comprehensive decision-making. Compared with satellite remote sensing，aerial platforms that typically employ unmanned aerial vehicles （UAVs） and low-altitude aircraft offer high spatial resolution and great observational flexibility，facilitating the completion of several tasks，such as fine urban modeling，small-object recognition，and emergency monitoring. However，these advantages also introduce severe scale variations，highly complex backgrounds，and inconsistent imaging conditions，making it extremely difficult for conventional models to capture both global scene semantics and subtle local textures simultaneously. Recent advances in deep learning and artificial intelligence （AI） have transformed remote sensing interpretation from manual inspection toward automation and intelligence，achieving notable results in scene classification，object detection，and semantic segmentation. Yet single-task and single-modality paradigms remain inadequate in terms of fully exploiting complementary information in heterogeneous multisource data. As such，these paradigms fall short of supporting the high-level semantic comprehension and integrated decision-making required for complex applications. The emergence of multimodal large models provides a transformative pathway for intelligent aerial remote sensing interpretation. These models can jointly perform image understanding and linguistic reasoning by integrating visual encoders with large language models（LLMs） through instruction tuning and cross-modal alignment，thus achieving breakthroughs in visual-language fusion，cross-modal reasoning，and task-instruction-guided analysis. At the same time，within the remote sensing community，researchers have begun to construct large-scale image-text datasets to train vision-language models and employ linguistic guidance to enhance the comprehension of complex land-cover semantics，thus marking a critical shift from purely visual modeling toward semantically augmented modeling and laying the groundwork for the subsequent development of multimodal large models. A systematic review reveals that the evolution of remote sensing large models has followed a clear paradigm progression： from early approaches that relied solely on single visual modalities，through vision-language models that introduced textual semantics but are still confined to specific tasks，to the current stage in which unified multimodal large models enable cross-task collaboration and complex reasoning. Despite these promising advances，the application of multimodal large models to aerial remote sensing still faces several formidable challenges. The substantial disparities in spatiotemporal references and radiometric properties among heterogeneous data sources，such as optical，SAR，and hyperspectral imagery，render cross-modal alignment extremely difficult，often giving rise to semantic misalignment and information loss. While fine-grained spatial structures and multiscale objects inherent in aerial imagery impose stringent demands on spatial cognition and multiscale reasoning，general-purpose large models often lack sufficient domain-specific spatial priors to accurately capture geometric and structural relationships. High-resolution scenes that impose massive computational and storage burdens also make real-time inference on edge devices highly challenging，resulting in the failure to meet the stringent timeliness and generalization requirements of time-sensitive tasks，such as disaster response and UAV-based inspection. Moreover，the black-box nature of model decision-making leads to insufficient interpretability and trustworthiness that hinders deployment in safety-critical scenarios. Meanwhile，data privacy concerns have become increasingly prominent within distributed collaborative learning frameworks. In response to the abovementioned challenges，this paper systematically reviews recent advances in multimodal data fusion and large model technologies for aerial remote sensing by tracing the shift from single-source to multimodal integration. Driven by large models，remote sensing interpretation has gradually evolved from low-level perception to cross-modal reasoning and semantic understanding. Nevertheless，substantial challenges remain in multimodal alignment，spatial cognition，task reliability，and practical deployment in real-world scenarios. To help resolve these problems，we analyze cross-sensor fusion and emphasize how disparities in geometry，radiometry，and acquisition timing among optical，SAR，and LiDAR data severely impede cross-modal semantic alignment. Furthermore，despite advances in vision-language pretraining，high resolution and complex backgrounds often degrade alignment accuracy，thus requiring fine-grained multimodal representations through geometric correction，radiometric normalization，and spatiotemporal consistency modeling. For multiscale spatial reasoning，we emphasize that complex tasks require holistic understanding of structural relations and spatial distributions. We argue that while existing models partially strengthen spatial reasoning via region-based interaction and spatial question answering，a unified framework for global spatial relation modeling is needed to elevate task awareness and multilevel reasoning. Generative AI for few-shot interpretation has the potential to alleviate annotation scarcity and support time-critical missions，such as disaster response. Regarding trustworthiness and interpretability，peculiar data distributions and insufficient instruction data in remote sensing exacerbate hallucinations in large vision-language models. Thus，domain knowledge，task constraints，and dedicated evaluation systems are required to enforce reasoning consistency and reliable high-stakes decisions. For efficient edge deployment and privacy，high-resolution large-format data impose heavy computational and storage burdens. Furthermore，although lightweight networks continue to emerge，trade-offs among real-time performance，stability，and complex scene adaptability remain，which means that model compression，inference optimization，and hardware-software co-design become critical. Related to this，federated learning offers a privacy-preserving mechanism for multisource data integration. In summary，addressing these challenges is important not only for advancing the theoretical understanding of cross-modal semantic reasoning and multiscale spatial cognition but also for facilitating the practical deployment of intelligent aerial remote sensing systems in high-stakes，real-world applications.

, authors=Jinwei Lang^*, Yaxin Li, Shuai Liu, Xiaodong Kang, Junqiang Cheng, authorsList=Jinwei Lang, Yaxin Li, Shuai Liu, Xiaodong Kang, Junqiang Cheng, authorCompany=null, correspAuthors=Jinwei Lang, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1322961792774833147, articleId=1322961736998978527, tenantId=1146029695717560320, journalId=1249024232475115590, language=CN, title=大模型赋能航空遥感智能解译的进展与思考, columnId=1322961356168765817, journalTitle=中国图象图形学报, columnName=大模型在航空领域的智慧赋能, runingTitle=null, highlight=null, articleAbstract=

随着航空遥感多源传感器的发展及多模态数据融合与智能解译技术的推进，航空遥感技术正经历从单一模态到多模态的深刻变革。这一进展为精准农业、城市环境监测、生态保护及自然灾害评估等领域的智能化监测与决策提供了重要的应用前景。传统的遥感影像解译方法主要依赖单源传感器数据和单任务学习模型，难以应对地表地物目标尺度多变、语义层次丰富、时空异质性强的复杂场景。近年来，多模态大模型的快速发展推动了遥感技术的显著进步，尤其在视觉—语言融合、跨模态推理，以及基于任务指令的跨模态分析与生成等方面取得了重要成果。然而，如何有效处理多源异构数据、提升模型的可解释性，同时保证实时推理能力，仍是当前面临的核心挑战。针对上述问题，对近年来航空遥感领域多模态数据融合及大模型技术的研究进展进行了系统评述，并深入分析了关键技术瓶颈与未来发展方向。研究重点包括跨传感器、多源及多尺度数据融合、空间语义推理、模型的可信推理与可解释性评估，以及在边缘计算环境下的高效部署与数据隐私保护策略。这些问题不仅是推动航空遥感智能解译技术从理论研究向实际应用转化的关键所在，也是实现技术落地并确保可持续发展的现实挑战。

, authors=郎晋伟^*, 李娅星, 刘帅, 康晓东, 程俊强, authorsList=郎晋伟, 李娅星, 刘帅, 康晓东, 程俊强, authorCompany=null, correspAuthors=郎晋伟, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=EYYtPamiHiqiKcvYRgGO4Q==, magXml=aqtKING0sRYZGu1R+1Ml8Q==, pdfUrl=null, pdf=xeqh9xfGnR1eNUtnG6sD+w==, pdfFileSize=1584325, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=mj0yuqlvXHH3YryJV7be4Q==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=PrjbjGOR6anPq/Z80dEFmg==, mapNumber=null, fund=null)}, authors=[Author(id=1322961796134469633, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322961736998978527, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=langjw@mail.ustc.edu.cn, emailSecond=null, emailThird=null, correspondingAuthor=1, authorType=1, ext={EN=AuthorExt(id=1322961797015273475, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322961736998978527, authorId=1322961796134469633, language=EN, stringName=Jinwei Lang, firstName=Jinwei, middleName=null, lastName=Lang, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=^*, address=Xi’an Aeronautics Computing Technique Research Institute，AVIC，Xi’an 710065，China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1322961797464064005, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322961736998978527, authorId=1322961796134469633, language=CN, stringName=郎晋伟, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=^*, address=中国航空工业集团西安航空计算技术研究所，西安　710065, bio={"content":"

郎晋伟，男，博士，工程师，主要研究方向为航空计算机视觉、飞行视觉引导。E-mail： langjw@mail.ustc.edu.cn

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郎晋伟，男，博士，工程师，主要研究方向为航空计算机视觉、飞行视觉引导。E-mail： langjw@mail.ustc.edu.cn

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李娅星，女，工程师，主要研究方向为飞控计算机设计和飞行视觉引导。E-mail： liyx174@avic.com

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李娅星，女，工程师，主要研究方向为飞控计算机设计和飞行视觉引导。E-mail： liyx174@avic.com

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刘帅，男，研究员，主要研究方向为嵌入式高可靠智能计算和飞行器管理计算平台。E-mail： liushuaijiayou@126.com

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刘帅，男，研究员，主要研究方向为嵌入式高可靠智能计算和飞行器管理计算平台。E-mail： liushuaijiayou@126.com

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康晓东，男，硕士，高级工程师，主要研究方向为高可靠多余度容错技术。E-mail： kangxd@avic.com

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康晓东，男，硕士，高级工程师，主要研究方向为高可靠多余度容错技术。E-mail： kangxd@avic.com

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程俊强，男，研究员，主要研究方向为容错计算技术。E-mail： junstrong@hotmail.com

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程俊强，男，研究员，主要研究方向为容错计算技术。E-mail： junstrong@hotmail.com

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模型类型	模型名称	训练方式	方法策略	核心优势	典型应用
视觉基础模型	Scale-MAE（Reed等，2023）	自监督预训练	多尺度感知编码	多尺度泛化强	场景分类
	RingMo-Aerial（Diao等，2024）	对比学习预训练	仿射增强 + 多尺度注意力	小目标表现佳	分类/检测
	AnySat （Astruc等，2025）	自监督预训练	多分辨率/多尺度/多模态建模	跨模态能力强	地表覆盖分类、变化检测、环境监测
视觉—语言大模型	GeoChat（Kuckreja等，2024）	指令微调	区域输入 + 空间定位	区域描述 + 对话 + 定位一体	区域解释、交互系统
	RemoteCLIP（Liu等，2024b）	图文对比学习	图文对齐	零样本分类能力强	开放类别分类、检索
	RSGPT（Hu等，2025）	高质量图文微调	视觉—语言对齐 + 指令微调	推动遥感标注标准化	图像描述、视觉问答
	RS-LLaVA（Bazi等，2024）	LoRA（low-rank adaptation）微调	视觉—语言融合 + 多任务指令微调	训练成本低、部署简单	多模态系统
	RS-MoE（Lin等，2025）	专家混合训练	多专家路由机制	高效 + 高性能平衡	高质量描述生成
任务特定模型	RAT（Zhao和Xiong，2024）	监督训练	区域特征 + 关系建模	局部目标描述精细	对象级描述
	RSVG（Zhan等，2023）	监督训练	多层跨模态对齐	跨模态定位，尺度与背景鲁棒	文本定位目标
	MTP（Wang等，2024a）	多任务预训练	分割 + 检测联合训练	与遥感任务一致性强	通用检测
	MSVG（Ding等，2025）	监督训练	多维跨模态交互	精细对齐能力强	小目标定位
	LAE-DINO（Pan等，2024）	大规模预训练	文本驱动检测	支持未知类别	开放世界检测

), ArticleFig(id=1322961819626766391, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322961736998978527, language=CN, label=表1, caption=

典型航空遥感大模型汇总与对比

, figureFileSmall=null, figureFileBig=null, tableContent=

模型类型	模型名称	训练方式	方法策略	核心优势	典型应用
视觉基础模型	Scale-MAE（Reed等，2023）	自监督预训练	多尺度感知编码	多尺度泛化强	场景分类
	RingMo-Aerial（Diao等，2024）	对比学习预训练	仿射增强 + 多尺度注意力	小目标表现佳	分类/检测
	AnySat （Astruc等，2025）	自监督预训练	多分辨率/多尺度/多模态建模	跨模态能力强	地表覆盖分类、变化检测、环境监测
视觉—语言大模型	GeoChat（Kuckreja等，2024）	指令微调	区域输入 + 空间定位	区域描述 + 对话 + 定位一体	区域解释、交互系统
	RemoteCLIP（Liu等，2024b）	图文对比学习	图文对齐	零样本分类能力强	开放类别分类、检索
	RSGPT（Hu等，2025）	高质量图文微调	视觉—语言对齐 + 指令微调	推动遥感标注标准化	图像描述、视觉问答
	RS-LLaVA（Bazi等，2024）	LoRA（low-rank adaptation）微调	视觉—语言融合 + 多任务指令微调	训练成本低、部署简单	多模态系统
	RS-MoE（Lin等，2025）	专家混合训练	多专家路由机制	高效 + 高性能平衡	高质量描述生成
任务特定模型	RAT（Zhao和Xiong，2024）	监督训练	区域特征 + 关系建模	局部目标描述精细	对象级描述
	RSVG（Zhan等，2023）	监督训练	多层跨模态对齐	跨模态定位，尺度与背景鲁棒	文本定位目标
	MTP（Wang等，2024a）	多任务预训练	分割 + 检测联合训练	与遥感任务一致性强	通用检测
	MSVG（Ding等，2025）	监督训练	多维跨模态交互	精细对齐能力强	小目标定位
	LAE-DINO（Pan等，2024）	大规模预训练	文本驱动检测	支持未知类别	开放世界检测

), ArticleFig(id=1322961820037808184, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322961736998978527, language=EN, label=Table 2, caption=A survey of aerial remote sensing datasets for vision-language modeling, figureFileSmall=null, figureFileBig=null, tableContent=

数据集	数据规模	主要任务类型	典型用途
RSICD（Lu等，2018）	10 921幅图像，每幅图像5条描述	图像描述、图文检索	遥感图像描述、图文匹配、跨模态检索
RSITMD（Yuan等，2022）	4 743幅图像，23 715条文本描述	图文检索、细粒度图文匹配	遥感跨模态检索、细粒度语义匹配
NWPU-Captions（Cheng等，2022）	31 500幅图像，157 500条描述	图像描述、图文检索	大规模遥感图像描述、图文检索
RSVQA（Lobry等，2020）	100 659幅图像，100 660 316条问答记录	视觉问答	遥感视觉问答、目标计数、属性识别、空间关系推理
FloodNet-VQA（Rahnemoonfar等，2021）	3 200幅图像，包含视觉问答数据	灾害场景视觉问答	灾害场景问答、灾后损毁识别、应急监测
DIOR-RSVG（Zhan等，2023）	17 402幅图像，38 320个图像—文本查询对	遥感数据视觉定位任务	文本引导目标定位、视觉锚定、小目标定位
LEVIR-CC（Liu等，2022）	10 077对双时相图像，50 385条变化描述	变化描述、双时相图像理解	变化描述、土地利用变化解释、灾害前后对比分析
RSIEval（Hu等，2025）	100条人工描述，936个开放式问答对	图像描述、视觉问答	遥感视觉语言模型综合评测、开放式问答、描述生成

), ArticleFig(id=1322961820167831609, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322961736998978527, language=CN, label=表2, caption=

支持视觉—语言建模的航空遥感数据集

, figureFileSmall=null, figureFileBig=null, tableContent=

数据集	数据规模	主要任务类型	典型用途
RSICD（Lu等，2018）	10 921幅图像，每幅图像5条描述	图像描述、图文检索	遥感图像描述、图文匹配、跨模态检索
RSITMD（Yuan等，2022）	4 743幅图像，23 715条文本描述	图文检索、细粒度图文匹配	遥感跨模态检索、细粒度语义匹配
NWPU-Captions（Cheng等，2022）	31 500幅图像，157 500条描述	图像描述、图文检索	大规模遥感图像描述、图文检索
RSVQA（Lobry等，2020）	100 659幅图像，100 660 316条问答记录	视觉问答	遥感视觉问答、目标计数、属性识别、空间关系推理
FloodNet-VQA（Rahnemoonfar等，2021）	3 200幅图像，包含视觉问答数据	灾害场景视觉问答	灾害场景问答、灾后损毁识别、应急监测
DIOR-RSVG（Zhan等，2023）	17 402幅图像，38 320个图像—文本查询对	遥感数据视觉定位任务	文本引导目标定位、视觉锚定、小目标定位
LEVIR-CC（Liu等，2022）	10 077对双时相图像，50 385条变化描述	变化描述、双时相图像理解	变化描述、土地利用变化解释、灾害前后对比分析
RSIEval（Hu等，2025）	100条人工描述，936个开放式问答对	图像描述、视觉问答	遥感视觉语言模型综合评测、开放式问答、描述生成

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