Collaborative Learning for Multimodal Remote Sensing Analysis: Methods, Techniques and Applications
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".
Deadline for manuscript submissions: 28 May 2025 | Viewed by 1657
Special Issue Editors
Interests: artificial intelligence (in particular, machine learning, multiagent systems and their applications); formal methods (in particular, machine-learning-based model checking)
Special Issues, Collections and Topics in MDPI journals
Interests: environmental change monitoring; machine learning/deep learning; geospatial artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Interests: artificial intelligence; evolutionary computation; metaheuristic learning; memetic computing; image processsing; hyperspectral image interpretation; change detection
Special Issue Information
Dear Colleagues,
Collaborative Learning for Multimodal Remote Sensing Analysis is a rapidly evolving field that integrates diverse sensing modalities to enhance the understanding and interpretation of complex environmental and urban phenomena. This interdisciplinary approach leverages the strengths of various sensors, including optical, infrared, LiDAR, and radar, to provide a comprehensive view of the observed scenes. Collaborative analysis of multimodal data is crucial for improving the accuracy and reliability of remote sensing applications, such as environmental monitoring, disaster response, and urban planning.
In this context, the collaborative learning paradigm plays a pivotal role in fusing data from different sources, overcoming the limitations of individual sensors, and extracting more meaningful insights. It involves the development of algorithms that can effectively combine information from multiple modalities, leading to enhanced feature representation and improved decision-making processes.
This Special Issue is designed to bring together cutting-edge research on collaborative learning methods, techniques, and applications in the domain of multimodal remote sensing analysis. We seek contributions that showcase innovative approaches and significant advancements in this area. Topics of interest include, but are not limited to, the following:
- Collaborative learning frameworks for multimodal data integration;
- Theoretical foundations and algorithms for collaborative learning in remote sensing;
- Applications of collaborative learning in environmental monitoring and ecological studies;
- Collaborative learning for urban analytics and smart city development;
- Multimodal data fusion techniques for disaster detection and management;
- Collaborative learning for agricultural and forestry monitoring;
- Advanced signal processing and feature extraction methods for collaborative learning;
- Performance metrics and evaluation protocols for collaborative learning in remote sensing.
We encourage the submission of research articles, review articles, and short communications that contribute to the advancement of collaborative learning in multimodal remote sensing analysis. Your contributions will not only enrich the body of knowledge in this field but also foster interdisciplinary collaboration and innovation.
Dr. Mingyang Zhang
Dr. Puzhao Zhang
Dr. Xiangming Jiang
Dr. Fenlong Jiang
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
- deep learning
- collaborative learning
- remote sensing analysis
- computational intelligence
- regular optimization
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