Deep Learning for Remote Sensing Image Processing: Challenges and Future Directions
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".
Deadline for manuscript submissions: 30 April 2026 | Viewed by 14
Special Issue Editors
Interests: crop mapping and crop type classification
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing image super-resolution; multi-modal learning
Special Issues, Collections and Topics in MDPI journals
Interests: time series information recovery; parameter-efficient fine-tuning; transfer learning
Interests: hyperspectral unmixing; optimization; image processing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The advent of deep learning has profoundly reshaped the landscape of remote sensing image analysis, delivering unprecedented performance in basic tasks such as image segmentation, object detection, and scene classification. This progress has paved the way for increasingly ambitious applications in environmental monitoring, urban management, and sustainable development. However, as we stand on the shoulders of these achievements, new frontiers and complex challenges emerge, driven by the deluge of multi-source, multi-modal, multi-temporal data and the pressing need for more precise, explainable, and physically grounded intelligent analysis. The path forward is fraught with intriguing questions. How do we overcome the perennial bottleneck of annotation scarcity for complex tasks like fine-grained crop classification and the inversion of continuous biogeophysical variables? Can the burgeoning development of foundation models, pre-trained on massive unlabeled data, become a generalized solution to a myriad of downstream tasks, or will their success be constrained by the unique characteristics of remote sensing data? Furthermore, how do we design models that can seamlessly reason across the complementary modalities of optical, SAR, and other sensing technologies? Beyond accuracy, how can we instill trust through explainability and embed physical principles into deep learning architectures to ensure that their outputs are not just statistically sound but also scientifically credible?
This Special Issue aims to present cutting-edge advances in deep learning methodologies for remote sensing image processing, highlighting innovative solutions to current challenges and exploring future directions. We welcome original research and comprehensive reviews addressing theoretical developments and practical applications.
Topics of interest include but are not limited to
- Novel network architectures;
- Limited annotation learning;
- Multi-modal data fusion;
- Remote sensing foundation models;
- Self-supervised learning;
- Domain adaptation;
- Model interpretability;
- Temporal sequence analysis;
- Physics-informed deep learning;
- Multi-task learning;
- Generative models.
Dr. Wenyuan Li
Dr. Sen Lei
Dr. Yuxiang Zhang
Dr. Longfei Ren
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
- foundation models
- data fusion
- self-supervised learning
- explainable AI (XAI)
- multi-temporal analysis
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