Multimodal Learning and Explainable AI for Remote Sensing Image Interpretation
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".
Deadline for manuscript submissions: 30 June 2026 | Viewed by 994
Special Issue Editors
Interests: remote sensing image quality improvement; calibration; intelligence processing
Interests: SAR; InSAR; PSInSAR; deformation monitoring; landslides; geohazards; GNSS; mobile surveying
Special Issues, Collections and Topics in MDPI journals
Interests: processing of the TomoSAR data and InSAR data for the forest structure estimation and the vegetation height retrieval
Special Issues, Collections and Topics in MDPI journals
Interests: GIS; urban data science; health geography; environmental epidemiology; transportation
Special Issues, Collections and Topics in MDPI journals
Interests: SAR calibration; soil moisture retrieval; multi-source remote sensing image processing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
With the development of modern space technology, remote sensing imagery has become increasingly prevalent. However, significant variances in radiometric and spatial properties across various types of remote sensing imagery pose major challenges for accurate image interpretation. Multimodal machine learning, which aims to process and integrate information from diverse modalities, offers a promising solution through machine learning approaches. Multimodal learning for the interpretation of remote sensing imagery is an emerging field in earth observation and computer vision. Given that multiple modalities contribute unequally to the final prediction, explainable AI will be necessary to elucidate their predicted contribution and identify potential biases in remote sensing image interpretation.
Multimodal machine learning remains demanding in the context of rapidly evolving remote sensing, and the complexity of current models often leads to limited explicability and transparency. This Special Issue aims to explore the recent advances, challenges, and practical applications of multimodal learning and explainable AI for interpreting multisource remote sensing imagery. Contributions may address new theories, methodologies, or applications of multimodal learning for the processing and analysis of remote sensing data, along with related challenges and opportunities.
Articles may address, but are not limited to, the following topics:
- Multimodal Fusion Architectures for Heterogeneous Remote Sensing Data;
- Cross-Modal Alignment, Registration Representation, and Translation Learning;
- Interpretable Multimodal Learning Frameworks in Remote Sensing;
- Explainable AI Techniques for Transparent Multisource Data Interpretation;
- Research on Multimodal Remote Sensing Image Matching;
- Domain Adaptation and Cross-Modal Transfer Learning for Remote Sensing Data;
- Multimodal Data Reconstruction and Quality Enhancement with Explainable Fusion Strategies;
- Application of Multimodal Machine Learning for Earth Observation: Case Studies in Land Use, Ecosystem Monitoring, and Disaster Response;
- Environmental Monitoring with Multimodal Learning Technology;
- Benchmarks, Datasets, and Evaluation Metrics for Multimodal Remote Sensing.
Dr. Le Yang
Prof. Dr. Xiaoli Ding
Dr. Lei Shi
Dr. Yuchen Li
Dr. Weidong Sun
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
- remote sensing
- multimodal machine learning
- explainable AI
- image interpretation
- data fusion
- heterogeneous data
- image registration
- earth observation
- environmental monitoring
- model interpretability
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.
Further information on MDPI's Special Issue policies can be found here.
