Advances in Deep Learning Approaches in Remote Sensing (Second Edition)
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 2025 | Viewed by 221
Special Issue Editors
Interests: intelligent optimization; machine learning; hyperspectral image processing
Special Issues, Collections and Topics in MDPI journals
Interests: machine learning and remote sensing image processing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Following the success of our previous Special Issue, “Advances in Deep Learning Approaches in Remote Sensing”, we are happy to announce that a new edition has been created.
Deep learning has witnessed an explosion of architectures of continuously growing capability and capacity. Benefiting from the rapid expansion of Earth observation data, deep learning has been effectively applied to various applications in remote sensing, including land use and land cover classification, scene classification, object detection, change detection, multimodal fusion, segmentation, and object-based image analysis. Nonetheless, as new challenges and opportunities emerge, the need for more advanced models, learning paradigms, and datasets to enable the efficient and effective processing and analysis of remote sensing data has become urgent.
This Special Issue aims to investigate the cutting-edge applications of deep learning in remote sensing. We invite research contributions and surveys in this area. Potential topics may include, but are not limited to, the following:
- Deep learning techniques for feature extraction of remote sensing data;
- Deep learning approaches for land cover and scene classification and clustering;
- Multimodal deep learning and the fusion of multimodal remote sensing data;
- Geometric deep learning for hyperspectral image processing;
- Super-resolution reconstruction based on deep learning methods;
- Change and object detection using deep learning methodologies;
- Self-/un-/semi-/supervised methods for interpretation of remote sensing data;
- Semantic segmentation of remote sensing images;
- New remote sensing datasets.
Dr. Xiaobo Liu
Dr. Yaoming Cai
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
- remote sensing image processing
- machine learning
- multimodal fusion
- representation learning
- intelligent interpretation
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Related Special Issue
- Advances in Deep Learning Approaches in Remote Sensing in Remote Sensing (14 articles)
