Multimodal AI-Empowered Remote Sensing: Image Fusion and Analysis
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".
Deadline for manuscript submissions: 31 January 2026 | Viewed by 146
Special Issue Editors
Interests: cross-media understanding and retrieval; multimodal personalized recommendation systems; multimodal semantic communication
Special Issues, Collections and Topics in MDPI journals
Interests: artificial intelligence; blockchain; wireless systems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Recent advancements in remote sensing have led to an unprecedented influx of large-scale, diverse multimodal data from sensors including optical, SAR, hyperspectral, LiDAR, and thermal systems. Effectively fusing and analyzing these multi-sensor data is crucial for critical applications such as land cover mapping, environmental monitoring, disaster response, and urban planning.
This Special Issue will focus on leveraging computer vision (CV) technologies, particularly deep learning and AI, to address the core challenges in remote sensing image fusion and analysis. We invite research on novel methodologies that address heterogeneous data integration, feature fusion, semantic understanding, and image enhancement. CV offers powerful capabilities for visual representation learning, cross-modal interpretation, and automating the analysis of complex remote sensing scenes, enabling deeper insights to be gained from multimodal data.
This Special Issue will bridge the gap between advanced computer vision methodologies and critical challenges in multimodal remote sensing image fusion and analysis. It will showcase cutting-edge research leveraging AI, deep learning, and computer vision to enable the robust integration, enhancement, and semantic interpretation of heterogeneous remote sensing data (e.g., optical, SAR, hyperspectral, and LiDAR data). Our goal is to collate innovative solutions that transform complex multi-sensor inputs into actionable geospatial insights.
This initiative directly aligns with Remote Sensing's core scope through its emphasis on interdisciplinary approaches and novel processing techniques. By advancing methodologies for data interpretation, enhancing application readiness, and addressing challenges in sensor diversity and big data in the observation of Earth, this Special Issue will support the journal's mission of improving geospatial knowledge extraction.
Articles may address, but are not limited, to the following topics:
The multimodal fusion of optical, SAR, hyperspectral, LiDAR, and textual data;
Cross-modal retrieval and matching in remote sensing;
Multimodal foundation models and large vision–language models;
The joint learning of enhancement and semantic understanding;
Super-resolution, deblurring, denoising, and cloud removal in RS images;
Adversarial and generative learning for RS data restoration;
Self-supervised and contrastive learning for multimodal remote sensing;
Multimodal scene interpretation under adverse conditions (e.g., occlusion, noise);
Domain adaptation and generalization across sensors and resolutions.
Dr. Jie Guo
Prof. Dr. F. Richard Yu
Dr. Yinghua Li
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- multimodal remote sensing
- cross-modal learning
- remote sensing image fusion
- image enhancement
- domain adaptation
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