Applications of Machine Learning Algorithms in Remote Sensing
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".
Deadline for manuscript submissions: closed (20 March 2025) | Viewed by 3881
Special Issue Editors
Interests: statistics; mathematics; GIS; remote sensing; image processing; machine learning; algorithm optimization
Interests: remote sensing; environmental change; grassland-wetland; ecosystems; precision agriculture; estuarine and coastal dynamics; remote sensing big data
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Deep learning-based techniques have been introduced in a wide range of applications, like the analysis of remote sensing images, owing to the rising accessibility of large-scale data sets, efficient training approaches, and high-performance computing devices. In the past few years, models based on deep learning have become a potent tool for analyzing imagery from satellites for a range of tasks, including classification, clustering, forecasting, and regression. Applying machine learning methods invented for computer vision to remote sensing data that is substantial, multivariate, noisy, and irregularly collected presents unseen challenges. Review and research papers on cutting-edge CNN and vision transformer-based methods for deep learning, architectures, and structures for applications in remote sensing will be published in this Special Issue, with an emphasis on tasks that address the problems in the field.
Potential topics of interest include, but are not limited to:
- Shallow and deep learning remote sensing image interpretation and analysis (image classification, pan-sharpening, image enhancement, object detection, semantic segmentation, and change detection)’
- Graph, adversarial, unsupervised, semi-supervised, self-supervised, active, and transfer learning for dealing with limited and/or low-quality data;
- Knowledge acquisition of deep learning models for remote sensing imagery;
- Novel benchmark datasets for remote sensing image analysis;
- Applications of vision transformers (ViTs) in remote sensing.
Dr. Ali Jamali
Dr. Bing Lu
Guest Editors
Manuscript Submission Information
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Keywords
- machine learning
- deep learning
- CNNs
- vision transformer
- geography
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