Neural Networks and Deep Learning for Satellite Image Processing
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".
Deadline for manuscript submissions: 15 July 2026 | Viewed by 892
Special Issue Editors
Interests: spatial and environmental big data processing methods; multisource spatial and environmental data assimilation and information fusion methods; digital soil mapping with remote sensing data; deep learning for land cover and land use mapping; Gaussian process for forest trait mapping
Special Issues, Collections and Topics in MDPI journals
Interests: satellite image data-based deep learning; spatial-temporal satellite image fusion and analysis; statistic learning; geostatistics; machine learning; Gaussian process; remote sensing technology-based digital mapping
Interests: multi- and hyper-spectral remote sensing data processing; high-resolution image processing and scene analysis; computational intelligence
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing land use and land cover classification methods and applications; hyper-spectral remote sensing information extraction; development and evaluation of sample databases for intelligent remote sensing image interpretation
Special Issue Information
Dear Colleagues,
Over the past decade, the rapid evolution of sophisticated AI technologies has significantly enhanced the precision and speed of analysing satellite remote sensing data, from basic classification tasks to more complex regression challenges. However, despite these advancements, contemporary remote sensing confronts persistent issues akin to those from a decade ago. Challenges persist in areas like the uncertainty surrounding multi-scale land cover mapping, integrating radiative transfer model simulations with deep learning algorithms, and establishing effective learning frameworks tailored to remote sensing tasks.
This Special Issue seeks original articles on creating innovative deep learning models for detecting various visual patterns aiding Earth monitoring. We also welcome research submissions on remote sensing change detection, forest canopy attribute retrieval, and ecosystem mapping with deep learning models. Additionally, we encourage in-depth review articles evaluating the effectiveness of cutting-edge deep learning models in remote sensing imagery.
Submitted works may explore various subjects, including the following:
- Robust retrieval of forest canopy structure or parameters based on deep learning;
- Deep learning-based land cover and land use change detection;
- Transformer-based classification and object detection;
- Reinforcement learning with remote sensing observation;
- Matric learning in feature space;
- Model generalisation;
- Transfer learning for large scale mapping;
- Transferability assessment.
Prof. Dr. Jinsong Chen
Dr. Shanxin Guo
Prof. Dr. Yanfei Zhong
Guest Editors
Dr. Yue Xu
Guest Editor Assistant
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
- forest canopy structure or parameter retrieval
- deep learning
- land cover and land use mapping
- transfer learning
- matric learning
- transferability assessment
- cloud removal
- spatial-temporal image fusion
- parameter efficient fine-tuning
- change detection
- object recognition
- super resolution
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