来源:Remote Sensing 发布时间:2026/10/16 14:23:57
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Remote Sensing 文献清单:"多模态遥感智能处理"方向

期刊名:Remote Sensing

期刊主页: https://www.mdpi.com/journal/remotesensing

近年来地球观测遥感数据量空前提高,综合协同处理多源遥感数据成为遥感领域的研究重点之一。本期文献清单围绕"多模态遥感智能处理"这一前沿方向精选了15篇相关文章,希望能为各位学者提供研究灵感。

1. DMDiff: A Dual-Branch Multimodal Conditional Guided Diffusion Model for Cloud Removal Through SAR-Optical Data Fusion

DMDiff:一种通过 SAR-光学数据融合实现云去除的双分支多模态条件引导扩散模型

https://www.mdpi.com/2072-4292/17/6/965

Zhang, W.; Mei, J.; Wang, Y. DMDiff: A Dual-Branch Multimodal Conditional Guided Diffusion Model for Cloud Removal Through SAR-Optical Data Fusion. Remote Sens. 2025, 17, 965.

2. Infrared-Visible Image Fusion Meets Object Detection: Towards Unified Optimization for Multimodal Perception

红外-可见光图像融合与目标检测:面向多模态感知的统一优化

https://www.mdpi.com/2072-4292/17/21/3637

Xiang, X.; Zhou, G.; Niu, B.; Pan, Z.; Huang, L.; Li, W.; Wen, Z.; Qi, J.; Gao, W. Infrared-Visible Image Fusion Meets Object Detection: Towards Unified Optimization for Multimodal Perception. Remote Sens. 2025, 17, 3637.

3. A Multimodal Ensemble Deep Learning Model for Wildfire Prediction in Greece Using Satellite Imagery and Multi-Source Remote Sensing Data

利用卫星影像和多源遥感数据进行希腊野火预测的多模态集成深度学习模型

https://www.mdpi.com/2072-4292/17/19/3310

Papakis, I.; Linardos, V.; Drakaki, M. A Multimodal Ensemble Deep Learning Model for Wildfire Prediction in Greece Using Satellite Imagery and Multi-Source Remote Sensing Data. Remote Sens. 2025, 17, 3310.

4. PR-CLIP: Cross-Modal Positional Reconstruction for Remote Sensing Image–Text Retrieval

PR-CLIP:用于遥感图像-文本检索的跨模态位置重建方法

https://www.mdpi.com/2072-4292/17/13/2117

Guan, J.; Shu, Y.; Li, W.; Song, Z.; Zhang, Y. PR-CLIP: Cross-Modal Positional Reconstruction for Remote Sensing Image–Text Retrieval. Remote Sens. 2025, 17, 2117.

5. MSFFNet: Multimodal Spatial–Frequency Fusion Network for RGB-DSM Remote Sensing Image Segmentation

MSFFNet:一种用于 RGB-DSM 遥感图像分割的多模态空间-频率融合网络

https://www.mdpi.com/2072-4292/17/22/3745

Zhi, Y.; Wang, Y.; Zhang, F.; Ma, M.; Mei, S. MSFFNet: Multimodal Spatial–Frequency Fusion Network for RGB-DSM Remote Sensing Image Segmentation. Remote Sens. 2025, 17, 3745.

6. NOAH: A Multi-Modal and Sensor Fusion Dataset for Generative Modeling in Remote Sensing

NOAH:面向遥感生成式建模的多模态与传感器融合数据集

https://www.mdpi.com/2072-4292/18/3/466

Mutakabbir, A.; Lung, C.-H.; Zaman, M.; Upadhyay, D.; Naik, K.; Millard, K.; Ravichandran, T.; Purcell, R. NOAH: A Multi-Modal and Sensor Fusion Dataset for Generative Modeling in Remote Sensing. Remote Sens. 2026, 18, 466.

7. Multimodal Prompt Tuning for Hyperspectral and LiDAR Classification

面向高光谱与 LiDAR 分类的多模态提示调优

https://www.mdpi.com/2072-4292/17/16/2826

Liu, Z.; Yuan, X.; Yang, S.; Fu, G.; Zhao, C.; Xiong, F. Multimodal Prompt Tuning for Hyperspectral and LiDAR Classification. Remote Sens. 2025, 17, 2826.

8. Can Separation Enhance Fusion? An Efficient Framework for Target Detection in Multimodal Remote Sensing Imagery

分离能否增强融合?多模态遥感影像目标检测的高效框架

https://www.mdpi.com/2072-4292/17/8/1350

Wang, Y.; Jia, J.; Liu, R.; Cao, Q.; Feng, J.; Li, D.; Wang, L. Can Separation Enhance Fusion? An Efficient Framework for Target Detection in Multimodal Remote Sensing Imagery. Remote Sens. 2025, 17, 1350.

9. SFANet: A Ground Object Spectral Feature Awareness Network for Multimodal Remote Sensing Image Semantic Segmentation

SFANet:一种用于多模态遥感图像语义分割的地面目标光谱特征感知网络

https://www.mdpi.com/2072-4292/17/10/1797

Lan, Y.; Zheng, D.; Zheng, Y.; Zhang, F.; Xu, Z.; Shang, K.; Wan, Z. SFANet: A Ground Object Spectral Feature Awareness Network for Multimodal Remote Sensing Image Semantic Segmentation. Remote Sens. 2025, 17, 1797.

10. GeoJapan Fusion Framework: A Large Multimodal Model for Regional Remote Sensing Recognition

GeoJapan 融合框架:一种用于区域遥感识别的大型多模态模型

https://www.mdpi.com/2072-4292/17/17/3044

Gan, Y.; Li, G.; Togo, R.; Maeda, K.; Ogawa, T.; Haseyama, M. GeoJapan Fusion Framework: A Large Multimodal Model for Regional Remote Sensing Recognition. Remote Sens. 2025, 17, 3044.

11. Physics-Driven Multi-Modal Fusion for SAR Ship Detection Under Motion Defocusing

运动散焦条件下基于物理驱动的多模态融合 SAR 舰船检测

https://www.mdpi.com/2072-4292/18/8/1166

Qiang, X.; Yu, Z.; Yao, X.; Li, D.; Deng, R.; Pu, N.; Zhong, S. Physics-Driven Multi-Modal Fusion for SAR Ship Detection Under Motion Defocusing. Remote Sens. 2026, 18, 1166.

12. WS-R-IR Adapter: A Multimodal RGB–Infrared Remote Sensing Framework for Water Surface Object Detection

WS-R-IR Adapter:一种用于水面目标检测的多模态 RGB-红外遥感框架

https://www.mdpi.com/2072-4292/18/8/1220

Xue, B.; Yu, Q.; Ding, K.; Jiang, M.; Wang, Y.; Xiang, S.; Pan, C. WS-R-IR Adapter: A Multimodal RGB–Infrared Remote Sensing Framework for Water Surface Object Detection. Remote Sens. 2026, 18, 1220.

13. Multimodal Uncertainty-Aware Gating Fusion and Iterative Feedback Refinement for HSI-LiDAR Open-Set Classification

面向 HSI-LiDAR 开放集分类的多模态不确定性感知门控融合与迭代反馈细化

https://www.mdpi.com/2072-4292/18/12/1963

Myagmarsuren, D.; Wu, H.; Wang, A. Multimodal Uncertainty-Aware Gating Fusion and Iterative Feedback Refinement for HSI-LiDAR Open-Set Classification. Remote Sens. 2026, 18, 1963.

14. LMFusion: Breaking the Computational Barrier for Multimodal Classification in Remote Sensing

LMFusion:突破遥感多模态分类的计算瓶颈

https://www.mdpi.com/2072-4292/18/12/1972

Zhou, S.; He, S.; Li, D.; Xie, W.; Li, Y. LMFusion: Breaking the Computational Barrier for Multimodal Classification in Remote Sensing. Remote Sens. 2026, 18, 1972.

15. MSF-Net: A Multimodal SAR–Optical Fusion Network for Agricultural Land Use Classification in Smallholder Landscapes of Northern Benin

MSF-Net:一种用于贝宁北部小农户景观农业土地利用分类的多模态 SAR-光学融合网络

https://www.mdpi.com/2072-4292/18/15/2622

Bio Nikki Sarè, S.B.; Gaetano, R.; Hountondji, Y.-C.; Interdonato, R. MSF-Net: A Multimodal SAR–Optical Fusion Network for Agricultural Land Use Classification in Smallholder Landscapes of Northern Benin. Remote Sens. 2026, 18, 2622.

期刊简介:

主编:Prasad S. Thenkabail, 美国地质调查局西部地理科学中心(WGSC),美国;

汪冬冬, 北京大学,中国

期刊范围涵盖遥感科学的各个方面,从传感器设计、验证和校准,到其在地球科学、环境科学、生态学和土木工程中的应用。

2025 Impact Factor:4.3

2025 CiteScore:9.4

Time to First Decision:22 Days

Acceptance to Publication:2.9 Days

 
 
 
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