Article(id=1322949811917423164, tenantId=1146029695717560320, journalId=1249024232475115590, issueId=1322949743743213946, articleNumber=null, orderNo=null, doi=10.11834/jig.260163, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1774800000000, receivedDateStr=2026-03-30, revisedDate=1776787200000, revisedDateStr=2026-04-22, acceptedDate=null, acceptedDateStr=null, onlineDate=1793345414380, onlineDateStr=2026-10-30, pubDate=1784131200000, pubDateStr=2026-07-16, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793345414380, onlineIssueDateStr=2026-10-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793345414380, creator=13701087609, updateTime=1793345414380, updator=13701087609, issue=Issue{id=1322949743743213946, tenantId=1146029695717560320, journalId=1249024232475115590, year='2026', volume='31', issue='7', pageStart='2279', pageEnd='2686', issueExtLink='null', onlineDate='null', pubDate='1784131200000', pubDateStr='2026-07-16', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793345398125, creator='13701087609', updateTime=1793348592997, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322963144217330568, tenantId=1146029695717560320, journalId=1249024232475115590, issueId=1322949743743213946, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322963144217330569, tenantId=1146029695717560320, journalId=1249024232475115590, issueId=1322949743743213946, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=2381, endPage=2407, ext={EN=ArticleExt(id=1322949812299104830, articleId=1322949811917423164, tenantId=1146029695717560320, journalId=1249024232475115590, language=EN, title=Advances in open vocabulary perception for remote sensing images, columnId=1249044010699002654, journalTitle=Journal of Image and Graphics, columnName=Review, runingTitle=null, highlight=null, articleAbstract=

Remote sensing technology serves as the core mechanism for the observation of the Earth and the understanding of surface environments. It plays an irreplaceable role in critical fields，such as natural disaster monitoring，urban planning，resource exploration，and ecological protection. Driven by the rapid advancement of deep learning over the past decade，the intelligent interpretation of remote sensing images has achieved breakthrough progress in fundamental vision tasks. However，the traditional deep learning paradigm is intrinsically built upon a close-set assumption，i.e.，that models can only recognize a predefined and human-annotated set of fixed categories during the inference stage. When confronted with highly complex surface environments in real-world Earth observation scenarios，dynamic object morphology，and rare ground objects with long-tail distributions，this traditional paradigm not only incurs prohibitive costs for the construction of massive pixel-level annotated datasets but also easily falls into the trap of domain-specific overfitting. Consequently，the generalization and response capabilities of this paradigm are severely challenged by unseen categories or sudden events，making this paradigm inadequate for meeting the highly dynamic interpretation demands of the open world. In recent years，the rapid development of vision-language models has catalyzed a paradigm shift in artificial intelligence from task-specific models into general-purpose perception models. By mapping visual representations and natural language into a unified feature space through contrastive learning on massive image-text pairs，these models have broken the constraints of discrete labels，enabling a direct response to arbitrary natural language prompts. This capability is known as open vocabulary perception. Although this technology has demonstrated remarkable zero-shot generalization and cross-modal reasoning capabilities in the natural image domain，the direct application of these general vision-language models to the remote sensing domain encounters a severe domain gap. The uniqueness of remote sensing data poses multiple challenges to the adaptability of existing models. First，the distinct overhead imaging perspective causes drastic variations in object scale and complex background textures. Second，Earth observation tasks rely on multisource heterogeneous data from synthetic-aperture radar，multispectral or hyperspectral imaging，and thermal infrared sensors. The underlying physical mechanisms of these sensors exceed the inherent inductive biases of models that are pretrained solely on natural RGB images. Third，remote sensing objects often exhibit strong geospatial attributes and complex topological associations. To address these critical challenges，this study provides a comprehensive and systematic review of recent advancements in open vocabulary perception for remote sensing images. We first delve into the foundational aspect of this field： vision-language pretraining for remote sensing. We extensively review the evolution of construction strategies for large-scale datasets. We highlight the transition from limited，human-annotated image-text pairs into massive datasets generated via heuristic rules，the integration of geographic metadata，and advanced multimodal large language models，including innovative approaches that leverage OpenStreetMap and geographical coordinates to produce fine-grained，physics-aware descriptions across multiple modalities. Concurrently，we systematically summarize the progression of pretraining methodologies. Although early approaches have primarily focused on simple domain adaptation through continuous pretraining，recent state-of-the-art frameworks emphasize physics-aware encoding，fine-grained multilevel consistency learning，and geography-enhanced architectures. These frameworks better capture the intricate spatial relationships and modality diversities that are inherent in Earth observation data. Subsequently，this review conducts an in-depth analysis of the adaptation and optimization of open vocabulary perception techniques across a wide spectrum of crucial downstream tasks. For zero-shot scene classification and cross-modal retrieval，we discuss advanced strategies that are designed to mitigate the high intra-class similarity and complex interclass variances typical in remote sensing. We emphasize the shift toward fine-grained local-global alignment，hard negative mining，dynamic soft labeling，and prompt engineering. In the realm of open vocabulary image segmentation，we categorize the existing literature into training-based methods and training-free or annotation-free paradigms. Training-based methods leverage base categories to adapt models while preventing catastrophic forgetting through pseudo-label distillation and knowledge retention mechanisms. Training-free paradigms synergize foundational models，such as CLIP and the segment anything model，to extract structural masks and align semantics without the updating of network weights. For open vocabulary object detection and remote sensing visual grounding，we explore the approaches of researchers to deal with extreme scale variations，arbitrary orientations，and dense object distributions. These approaches include innovative frameworks for pseudo-label generation，multi-scale feature alignment，cross-modality context modeling，and interactive grounding mechanisms. Furthermore，we examine open vocabulary change detection，wherein recent studies employ either combinations of pretrained vision-language models or generative models to generate large-scale data. These approaches aim to identify arbitrary，text-specified surface transitions and simulate complex spatiotemporal changes without reliance on massive and costly bitemporal pixel-level annotations. We also briefly discuss emerging open vocabulary applications in 3D urban point clouds and cross-domain archeological remote sensing，illustrating the expanding horizon of this technology. Despite remarkable progress，the field of open vocabulary perception for remote sensing remains in a crucial developmental stage and faces several critical bottlenecks. This study critically identifies the limitations of current research，including the severe scarcity of high-quality and geographically balanced training data. This scarcity leads to geographic biases and performance degradation in data-poor regions. In addition，genuinely fine-grained and long-tailed open vocabulary evaluation benchmarks that can accurately reflect the performance of a model are prominently absent in extreme or unknown real-world scenarios. The inadequate physical understanding of heterogeneous modalities and the inherent black box unreliability of current large models in high-stake decision-making scenarios further constrain practical deployments. To chart the course for future research，we outline several promising and essential trajectories. First，we anticipate a paradigm shift toward generative perception that is driven by multimodal large language models. This shift unifies various spatial localization tasks into the direct generation of coordinate sequences or geometric property tokens to utilize fully the logical reasoning capabilities of foundational models. Second，we strongly advocate for the construction of rigorous，real-world，and fine-grained evaluation systems that incorporate complex spatiotemporal logic，diverse geographic conditions，and comprehensive evaluation metrics. Third，the development of omni-modal foundation models that explicitly integrate physical priors and deep learning is considered crucial for the achievement of all-weather and all-spectrum Earth observations，moving beyond pure data-driven approaches. Furthermore，we highlight the necessity to extend perception from static spatial analysis to dynamic spatiotemporal causal reasoning to decode the evolutionary processes of the Earth. Finally，addressing the severe conflict between the massive parameter scale of foundation models and the limited computing power of aerospace edge devices requires focused research into efficient，trustworthy，and safe edge-cloud collaborative computing architectures. By systematically synthesizing these advancements and challenges，this comprehensive review aims to serve as a foundational road map for researchers and practitioners. It accelerates the transition of the intelligent interpretation of remote sensing from isolated，close-set recognition toward artificial general intelligence that is capable of highly reliable，dynamic，and open world perception.

, authors=Kaiyu Li1, Xiangyong Cao2^{, *}, Zixuan Jiang3, Deyu Meng4, authorsList=Kaiyu Li, Xiangyong Cao, Zixuan Jiang, Deyu Meng, authorCompany=null, correspAuthors=Xiangyong Cao, 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=1322949826060616267, articleId=1322949811917423164, tenantId=1146029695717560320, journalId=1249024232475115590, language=CN, title=遥感图像开放词汇感知进展, columnId=1249044010992603937, journalTitle=中国图象图形学报, columnName=综述, runingTitle=null, highlight=null, articleAbstract=

传统的遥感图像智能解译技术大多建立在封闭集假设之上，高度依赖海量的人工标注数据，且在推理阶段仅能识别训练集中预先定义的固定类别。面对真实地球观测场景中复杂多变的地表环境、尺度剧烈变化的目标以及长尾分布的罕见地物，传统范式泛化能力受限，难以满足高度动态的开放世界解译需求。近年来，得益于视觉—语言基础模型的快速发展，开放词汇感知技术应运而生。该技术通过跨模态语义对齐打破了传统离散标签的束缚，在零样本与少样本场景下展现出强大的泛化潜力。然而，遥感影像独特的俯视成像视角、复杂的拓扑关联以及多源异构的物理模态，致使自然图像领域的通用大模型在向遥感垂直领域迁移时面临显著的领域鸿沟。为此，本文系统梳理并总结了遥感图像开放词汇感知领域的最新研究进展。从数据和方法两个维度，阐述了遥感视觉—语言预训练数据集的构建策略，以及预训练架构从基础域适配向异构数据感知与地理先验增强的演进脉络；全面剖析了开放词汇感知在零样本场景分类、跨模态检索、图像分割、目标检测与定位、变化检测以及三维点云理解等关键下游任务中的应用范式；深入探讨了当前该领域在高质量训练数据匮乏、细粒度评测基准缺失、多源异构模态深层对齐不足及模型可靠性等方面面临的核心挑战，并从多模态大语言模型驱动的生成式感知、全模态基础模型演进、时空因果推演及星地协同计算等方向对未来发展趋势进行了系统展望，以期为推动遥感智能解译迈向真实开放世界提供详实的理论参考。

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李开宇，男，博士研究生，主要研究方向为计算机视觉和遥感图像解译。E-mail： likyoo.ai@gmail.com

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李开宇，男，博士研究生，主要研究方向为计算机视觉和遥感图像解译。E-mail： likyoo.ai@gmail.com

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曹相湧，通信作者，男，副教授，主要研究方向为机器学习和计算机视觉。E-mail： caoxiangyong@mail.xjtu.edu.cn

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曹相湧，通信作者，男，副教授，主要研究方向为机器学习和计算机视觉。E-mail： caoxiangyong@mail.xjtu.edu.cn

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蒋梓轩，男，本科生，主要研究方向为遥感多模态大模型。E-mail： andrewjiang@stu.xjtu.edu.cn

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蒋梓轩，男，本科生，主要研究方向为遥感多模态大模型。E-mail： andrewjiang@stu.xjtu.edu.cn

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孟德宇，男，教授，主要研究方向为机器学习和计算机视觉。E-mail： dymeng@mail.xjtu.edu.cn

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孟德宇，男，教授，主要研究方向为机器学习和计算机视觉。E-mail： dymeng@mail.xjtu.edu.cn

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UniVCD： a new method for unsupervised change detection in the open-vocabulary era［EB/OL］. ［2026-03-20］., articleTitle=UniVCD： a new method for unsupervised change detection in the open-vocabulary era, refAbstract=null)], funds=[Fund(id=1322949832104608393, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322949811917423164, awardId=null, language=EN, fundingSource=Fundamental and Interdisciplinary Disciplines Breakthrough Plan of the Ministry of Education of China （JYB2025XDXM101）；National Natural Science Foundation of China （62272375）；Tianyuan Fund for Mathematics of the National Natural Science Foundation of China （12426105）, fundOrder=null, country=null), Fund(id=1322949832171717258, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322949811917423164, awardId=null, language=CN, fundingSource=教育部学科先导计划项目（JYB2025XDXM101）；国家自然科学基金项目（62272375）；国家自然科学基金数学天元基金项目（12426105）, fundOrder=null, country=null)], 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数据集	年份	图像数量	图文对数量	分辨率	空间覆盖	图像模态	注释方法
UCM-Captions	2016	2 k	10 k	0.3 m	美国	光学 (RGB)	人工标注
Sydney-Captions	2016	0.6 k	3 k	0.5 m	悉尼	光学 (RGB)	人工标注
RSICD	2017	10 k	54 k	多种分辨率	-	光学 (RGB)	人工标注
RSTeller	2024	1.2 M	2.5 M	0.6 m	美国	光学 (RGB)	自动标注
RS5M	2024	5 M	5 M	多种分辨率	全球	光学 (RGB)	自动标注
SkyScript	2024	2.6 M	2.6 M	0.1～30 m	全球	光学 (RGB)	自动标注
LuojiaHOG	2024	94 k	94 k	多种分辨率	全球	光学 (RGB)	混合标注
RSM-ITD	2024	210 k	476 k	多种分辨率	全球	光学 (RGB)	自动标注
Git-10M	2025	10 M	10 M	0.5～128 m	全球	光学 (RGB)	自动标注
GAIA	2025	40 k	201 k	多种分辨率	全球	多模态视觉组合	自动标注
HQRS-IT-210K	2025	210 k	1.3 M	多种分辨率	全球	光学 (RGB与MSI)	自动标注
DGTRSD	2025	1 762 k	1 762 k	0.08～153 m	全球	光学 (RGB)	自动标注
MMSAR	2025	105 k	296 k	0.1～25 m	全球	SAR	自动标注
SARVLM-1M	2025	345 k	1.7 M	多种分辨率	全球	SAR	自动标注
SAR-TEXT	2025	136 k	136 k	多种分辨率	全球	SAR	自动标注
Llama3-SSL4EO-S12	2025	975 k	975 k	10 m	全球	光学 (MSI), SAR	自动标注
GeoLangBind-2M	2025	2 050 k	2 050 k	多种分辨率	全球	RGB, MSI, HSI, SAR, DEM, Infrared	自动标注
MGRS-200k	2025	200 k	400 k	多种分辨率	全球	光学 (RGB)	自动标注
RSFG-100k	2026	100 k	400 k	多种分辨率	全球	光学 (RGB)	自动标注

), ArticleFig(id=1322949831202833028, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322949811917423164, language=CN, label=表1, caption=

遥感视觉—语言预训练数据集

, figureFileSmall=null, figureFileBig=null, tableContent=

数据集	年份	图像数量	图文对数量	分辨率	空间覆盖	图像模态	注释方法
UCM-Captions	2016	2 k	10 k	0.3 m	美国	光学 (RGB)	人工标注
Sydney-Captions	2016	0.6 k	3 k	0.5 m	悉尼	光学 (RGB)	人工标注
RSICD	2017	10 k	54 k	多种分辨率	-	光学 (RGB)	人工标注
RSTeller	2024	1.2 M	2.5 M	0.6 m	美国	光学 (RGB)	自动标注
RS5M	2024	5 M	5 M	多种分辨率	全球	光学 (RGB)	自动标注
SkyScript	2024	2.6 M	2.6 M	0.1～30 m	全球	光学 (RGB)	自动标注
LuojiaHOG	2024	94 k	94 k	多种分辨率	全球	光学 (RGB)	混合标注
RSM-ITD	2024	210 k	476 k	多种分辨率	全球	光学 (RGB)	自动标注
Git-10M	2025	10 M	10 M	0.5～128 m	全球	光学 (RGB)	自动标注
GAIA	2025	40 k	201 k	多种分辨率	全球	多模态视觉组合	自动标注
HQRS-IT-210K	2025	210 k	1.3 M	多种分辨率	全球	光学 (RGB与MSI)	自动标注
DGTRSD	2025	1 762 k	1 762 k	0.08～153 m	全球	光学 (RGB)	自动标注
MMSAR	2025	105 k	296 k	0.1～25 m	全球	SAR	自动标注
SARVLM-1M	2025	345 k	1.7 M	多种分辨率	全球	SAR	自动标注
SAR-TEXT	2025	136 k	136 k	多种分辨率	全球	SAR	自动标注
Llama3-SSL4EO-S12	2025	975 k	975 k	10 m	全球	光学 (MSI), SAR	自动标注
GeoLangBind-2M	2025	2 050 k	2 050 k	多种分辨率	全球	RGB, MSI, HSI, SAR, DEM, Infrared	自动标注
MGRS-200k	2025	200 k	400 k	多种分辨率	全球	光学 (RGB)	自动标注
RSFG-100k	2026	100 k	400 k	多种分辨率	全球	光学 (RGB)	自动标注

), ArticleFig(id=1322949831274136197, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322949811917423164, language=EN, label=Table 2, caption=Comparison of zero-shot scene classification methods for remote sensing images, figureFileSmall=null, figureFileBig=null, tableContent=

方法	年份	AID	EuroSAT	PatternNet	RESISC	方法	年份	AID	EuroSAT	PatternNet	RESISC
ViT-base:						ViT-large:
CLIP	2021	69.60	32.10	64.10	65.70	CLIP	2021	69.30	41.90	71.40	66.70
RemoteCLIP	2024	87.10	30.70	56.10	67.90	RemoteCLIP	2023	70.90	27.80	61.90	74.30
GeoRSCLIP	2023	70.30	53.40	-	68.80	GeoRSCLIP	2023	74.40	59.90	77.40	73.80
SkyCLIP-50	2024	70.90	33.30	72.20	66.70	SkyCLIP-50	2024	71.70	51.30	80.90	70.90
RS-M-CLIP	2024	88.90	25.90	51.00	92.60	RSTeller	2024	66.80	56.40	78.90	69.60
RSTeller	2025	63.60	38.40	64.70	58.50	RS-TransCLIP	2025	80.40	72.70	93.10	86.70
RS-TransCLIP	2025	78.20	69.00	-	79.50	DOFA-CLIP	2025	75.50	59.00	80.20	73.20
DOFA-CLIP	2025	77.60	52.30	76.70	67.20	DGTRS-CLIP	2025	-	55.30	76.70	76.30
DGTRS-CLIP	2025	-	48.50	74.60	73.60	HQRS-CLIP	2025	73.90	60.60	-	78.40
DALIP	2025	75.60	64.70	-	72.90	VLM2GeoVec	2025	77.80	39.90	79.80	-
RSCLIP	2025	86.70	48.40	66.70	68.60	Barzilai等人（2025）	2025	72.00	-	-	72.30

), ArticleFig(id=1322949831488045702, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322949811917423164, language=CN, label=表2, caption=

遥感图像零样本场景分类方法对比

, figureFileSmall=null, figureFileBig=null, tableContent=

方法	年份	AID	EuroSAT	PatternNet	RESISC	方法	年份	AID	EuroSAT	PatternNet	RESISC
ViT-base:						ViT-large:
CLIP	2021	69.60	32.10	64.10	65.70	CLIP	2021	69.30	41.90	71.40	66.70
RemoteCLIP	2024	87.10	30.70	56.10	67.90	RemoteCLIP	2023	70.90	27.80	61.90	74.30
GeoRSCLIP	2023	70.30	53.40	-	68.80	GeoRSCLIP	2023	74.40	59.90	77.40	73.80
SkyCLIP-50	2024	70.90	33.30	72.20	66.70	SkyCLIP-50	2024	71.70	51.30	80.90	70.90
RS-M-CLIP	2024	88.90	25.90	51.00	92.60	RSTeller	2024	66.80	56.40	78.90	69.60
RSTeller	2025	63.60	38.40	64.70	58.50	RS-TransCLIP	2025	80.40	72.70	93.10	86.70
RS-TransCLIP	2025	78.20	69.00	-	79.50	DOFA-CLIP	2025	75.50	59.00	80.20	73.20
DOFA-CLIP	2025	77.60	52.30	76.70	67.20	DGTRS-CLIP	2025	-	55.30	76.70	76.30
DGTRS-CLIP	2025	-	48.50	74.60	73.60	HQRS-CLIP	2025	73.90	60.60	-	78.40
DALIP	2025	75.60	64.70	-	72.90	VLM2GeoVec	2025	77.80	39.90	79.80	-
RSCLIP	2025	86.70	48.40	66.70	68.60	Barzilai等人（2025）	2025	72.00	-	-	72.30

), ArticleFig(id=1322949831894893191, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322949811917423164, language=EN, label=Table 3, caption=Comparison of open-vocabulary semantic segmentation methods for remote sensing images, figureFileSmall=null, figureFileBig=null, tableContent=

类别	方法	年份	数据集
			LoveDA	iSAID	Postdam	Vaihingen	UAVid	VDD	UDD5
基于训练 的方法	Cat-Seg	2023	36.9	21.7	47.1	29.1	-	-	-
	OVRS	2025	28.7	39.1	27.5	33.7	25.2	37.3	39.1
	GSNet	2025	29.3	42.0	26.5	35.2	25.4	38.1	40.1
	RSKT-Seg	2025	32.5	44.0	34.5	37.2	28.1	41.2	43.0
	SGSeg	2025	26.4	42.3	32.5	37.8	-	-	-
	FarSLIP	2025	34.2	18.7	47.5	22.8	40.0	40.8	44.8
	ROSS（Zhang等，2026）	2026	-	40.8	30.4	34.0	-	-	-
	SkySense-O	2025	38.3	43.9	54.1	51.6	-	-	-
免训练或免标注的方法	SegEarth-OV	2025	36.9	21.7	48.5	40.0	42.5	45.3	50.6
	RSCLIP	2025	38.0	-	47.4	28.9	-	-	50.8
	SegEarth-OV-3	2025	47.4	27.6	57.8	60.8	54.7	64.5	71.7
	AlignCLIP	2026	39.5	23.6	47.9	34.5	44.4	48.4	51.8
	Sosa等人（2026）	2026	38.2	21.9	50.2	40.6	44.3	46.8	53.8
	ReSeg-CLIP	2026	-	-	38.3	-	-	-	43.2
	SDCI	2026	43.7	46.4	47.5	50.1	-	-	-

), ArticleFig(id=1322949831970390664, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322949811917423164, language=CN, label=表3, caption=

遥感图像开放词汇语义分割方法对比

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类别	方法	年份	数据集
			LoveDA	iSAID	Postdam	Vaihingen	UAVid	VDD	UDD5
基于训练 的方法	Cat-Seg	2023	36.9	21.7	47.1	29.1	-	-	-
	OVRS	2025	28.7	39.1	27.5	33.7	25.2	37.3	39.1
	GSNet	2025	29.3	42.0	26.5	35.2	25.4	38.1	40.1
	RSKT-Seg	2025	32.5	44.0	34.5	37.2	28.1	41.2	43.0
	SGSeg	2025	26.4	42.3	32.5	37.8	-	-	-
	FarSLIP	2025	34.2	18.7	47.5	22.8	40.0	40.8	44.8
	ROSS（Zhang等，2026）	2026	-	40.8	30.4	34.0	-	-	-
	SkySense-O	2025	38.3	43.9	54.1	51.6	-	-	-
免训练或免标注的方法	SegEarth-OV	2025	36.9	21.7	48.5	40.0	42.5	45.3	50.6
	RSCLIP	2025	38.0	-	47.4	28.9	-	-	50.8
	SegEarth-OV-3	2025	47.4	27.6	57.8	60.8	54.7	64.5	71.7
	AlignCLIP	2026	39.5	23.6	47.9	34.5	44.4	48.4	51.8
	Sosa等人（2026）	2026	38.2	21.9	50.2	40.6	44.3	46.8	53.8
	ReSeg-CLIP	2026	-	-	38.3	-	-	-	43.2
	SDCI	2026	43.7	46.4	47.5	50.1	-	-	-

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