Multi-Platform and Multi-Modal Remote Sensing Data Fusion with Advanced Deep Learning Techniques (Third Edition)
A Special Issue of Remote Sensing (ISSN 2072-4292) belonging to the section "Remote Sensing Image Processing".
Deadline for manuscript submissions: 28 February 2027 | Viewed by 703
Editors
Interests: computer vision; multimedia forensics; digital
Special Issues, Collections and Topics in MDPI journals
Interests: machine learning; pattern recognition and computer vision
Special Issues, Collections and Topics in MDPI journals
Interests: hyperspectral remote sensing image processing (including: unmixing, classification, fusion); deep learning
Special Issues, Collections and Topics in MDPI journals
Interests: hyperspectral imagery; image denoising; spectroscopy
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
After the resounding success of the first and second edition of our Special Issue, “Multi-Platform and Multi-Modal Remote Sensing Data Fusion with Advanced Deep Learning Techniques”, we are thrilled to launch its third edition.
Recent advances in sensor and aircraft technology have enabled us to acquire vast amounts of different types of remote sensing data for Earth observation. These multi-source data make it possible to derive diverse information regarding the Earth's surface. For instance, multispectral and hyperspectral images can provide rich spectral information about ground objects, panchromatic images can reach fine spatial resolutions, synthetic aperture radar (SAR) data can be used to map different properties of the terrain, and laser imaging detection and ranging (LIDAR) data can clarify the elevation of land cover. However, a single source of data can no longer meet the needs of subsequent processing, such as classification, object detection/tracking, super-resolution, and restoration.
Therefore, multi-modal remote sensing data, acquired using sensors from multiple platforms, should be combined and fused. This fusion can make full use of the complementary information of multi-source remote sensing data, thereby further improving the accuracy of the analysis of the acquired scene (classification, detection, tracking, geological mapping, etc.).
Recently, deep learning has become one of the hottest research fields. Many advanced deep learning techniques have been developed, such as meta learning, self-supervision learning, few-shot learning, evolutionary learning, attention mechanisms, transformer, etc. The application of these technologies to remote sensing images, especially the fusion of multi-platform and multi-modal remote sensing data, is still an open topic. For this Special Issue, we seek original contributions (including high-quality original research articles, reviews, theoretical and critical perspectives, and viewpoint articles) written by innovative researchers on the fusion of multi-platform and multi-modal remote sensing data, which exploits advanced deep learning techniques to address the aforementioned theoretical and practical problems.
Prof. Dr. Yuhui Zheng
Dr. Guoqing Zhang
Dr. Le Sun
Prof. Dr. Byeungwoo Jeon
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-anonymized 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
- multispectral and hyperspectral data fusion
- hyperspectral and LiDAR data fusion
- pansharpening or thermal sharpening
- optical and SAR data fusion
- optical and LiDAR data fusion
- novel benchmark multi-platform or multi-modal datasets
- advanced deep learning algorithm/architectures/theory
- transfer, multitask, few-shot, and meta learning
- attention mechanism and transformer
- convolutional neural networks/graph convolutional networks
- scene/object classification and segmentation
- target detection/tracking
- geological mapping
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.
