Cross-Modal Learning and Pattern Recognition in Multisource Remote Sensing
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".
Deadline for manuscript submissions: 15 September 2026 | Viewed by 129
Special Issue Editors
Interests: RGB-D image; segmentation; LiDAR; ground intelligent robot
Interests: pattern recognition; computer vision; remote sensing
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing image processing and analysis
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Multisource remote sensing has revolutionized our ability to observe and understand Earth’s surface and atmosphere by providing complementary data from a variety of sensors and platforms. The integration of optical, radar, LiDAR, thermal, SAR, and hyperspectral data offers unprecedented opportunities to characterize environmental and anthropogenic processes with enhanced accuracy, robustness, and spatial–temporal coverage. However, effectively fusing these heterogeneous data sources remains a significant challenge, necessitating advanced computational approaches that can learn cross-modal representations and recognize complex patterns across different spectral, spatial, and structural domains.
In recent years, cross-modal learning has emerged as a powerful paradigm in remote sensing, enabling models to leverage complementary information from disparate data types, translate between modalities, and improve generalization in tasks such as classification, detection, segmentation, and change monitoring. Coupled with advances in pattern recognition—driven by deep learning, graph neural networks, attention mechanisms, and explainable AI—these methods are unlocking new possibilities in land cover mapping, disaster response, urban planning, agriculture, forestry, oceanography, and climate studies.
This Special Issue invites original research and review articles that explore innovative methodologies, applications, and case studies in cross-modal learning and pattern recognition using multisource remote sensing data. We welcome contributions that address both theoretical advances and practical implementations, with an emphasis on scalable, interpretable, and transferable solutions. Topics of interest include, but are not limited to:
- Cross-modal data fusion and alignment;
- Multimodal representation learning and feature extraction;
- Self-supervised and few-shot learning across modalities;
- Domain adaptation and generalization in multisource settings;
- Pattern recognition for land use/land cover classification;
- Object detection and segmentation from multimodal imagery;
- Change detection and time-series analysis with heterogeneous data;
- Explainable AI and interpretability in cross-modal models;
- Benchmark datasets and evaluation metrics for multimodal remote sensing;
- Applications in environmental monitoring, precision agriculture, urban studies, disaster management, unmanned systems, and climate research.
Dr. Xia Yuan
Dr. Mohamed Lamine Mekhalfi
Prof. Dr. Yakoub Bazi
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 250 words) can be sent to the Editorial Office for assessment.
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
- cross-modal learning
- multisource remote sensing
- data fusion
- pattern recognition
- deep learning
- multimodal representation
- feature extraction
- domain adaptation
- explainable AI
- environmental monitoring
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