New Deep Learning Paradigms for Multisource Remote Sensing Data Fusion and Classification
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 August 2024 | Viewed by 1579
Special Issue Editors
Interests: AI internet of things; machine learning; satellite remote sensing
Interests: remote sensing; computer vision; deep learning
Interests: computer vision; pattern recognition; machine learning; deep learning; sensor reconfiguration; anomaly detection
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Leveraging multisource remote sensing images for earth mapping and monitoring has drawn significant attention in large-scale geoscience applications. Numerous methods have been developed for data fusion and classification based on novel deep learning models in a fully supervised training manner. Although deep learning has shown dominance in multisource image fusion and classification, it still encounters several issues in practice, such as limited labeled samples in model training, weak representative capability for multisource data from heterogeneous domains, and performance degradation in cross-domain tasks. Some new learning paradigms have emerged to address these problems and to promote multimodal collaboration and cross-modal analysis in remote sensing, including self-supervised, weakly supervised, transfer, and federated learning. They significantly improve the generalization and robustness of deep learning models and provide new possibilities and challenges for the use of novel training and optimization algorithms in remote sensing.
This Special Issue aims to highlight the innovative research in novel deep learning paradigms for multisource remote sensing data fusion and classification. Topics may cover anything from multisource data integration to applications in remote sensing. Advanced deep models with new training and optimization strategies exploited in remote sensing applications are welcome to be submitted to this Special Issue.
Articles may address, but are not limited to, the following topics:
- Novel fusion strategies for multisource remote sensing data;
- Weakly and self-supervised deep learning in remote sensing image classification;
- Remote sensing data fusion with generative models;
- Transfer learning in remote sensing image classification and segmentation;
- Cross-modal analysis in remote sensing;
- Land-cover/use mapping using multisource remote sensing data;
- Multisource remote sensing applications.
Dr. Man On Pun
Dr. Xiaokang Zhang
Dr. Claudio Piciarelli
Dr. Libo Wang
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
- multi-modal fusion
- cross-modal analysis
- deep transfer learning
- domain adaptation
- semi-supervised learning
- active learning
- federated learning
- weakly supervised learning
- self-supervised learning
- few-shot learning
- unsupervised representation learning
- adversarial training
- generative models
- semantic segmentation
- scene classification
- land cover mapping
- change detection
- pansharpening
