Multi-Task Remote Sensing Image Analysis: Classification, Segmentation, and Change Detection
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 March 2025 | Viewed by 13
Special Issue Editor
Special Issue Information
Dear Colleagues,
The advancements in remote sensing technologies have opened new possibilities for multi-task image analysis, offering opportunities for solving complex challenges in classification, segmentation, and change detection. This Special Issue, Multi-Task Remote Sensing Image Analysis: Classification, Segmentation, and Change Detection, aims to bring together innovative research addressing the integration of these tasks to improve accuracy and efficiency in remote sensing applications. These multi-task approaches are crucial for various domains such as environmental monitoring, land use analysis, disaster management, and urban planning.
The goal of this Special Issue is to present state-of-the-art methods that unify or enhance different image analysis tasks, such as scene classification, semantic segmentation, and temporal change detection, within the remote sensing domain. We invite researchers to contribute novel algorithms, models, and frameworks that advance the field of multi-task learning and facilitate comprehensive and scalable remote sensing image analysis.
Topics of interest include, but are not limited to, the following:
- Multi-task learning for remote sensing image analysis;
- Joint classification and segmentation techniques;
- Change detection in multi-temporal remote sensing imagery;
- Deep learning methods for multi-task image processing;
- Data fusion for improved classification and change detection;
- Transfer learning in multi-task remote sensing applications;
- Applications of AI in multi-task remote sensing;
- High-resolution and large-scale remote sensing analysis;
- Challenges in imbalanced data and multi-class segmentation.
This Special Issue encourages submissions that address both theoretical innovations and practical applications, providing a platform for researchers and professionals to discuss emerging trends and future directions in multi-task remote sensing image analysis.
Dr. Liguo Weng
Guest Editor
Manuscript Submission Information
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Keywords
- multi-task learning
- remote sensing image classification
- semantic segmentation
- change detection
- deep learning
- multi-temporal analysis
- data fusion
- transfer learning
- environmental monitoring
- high-resolution remote sensing
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