Explainable Deep Neural Networks for Remote Sensing Image Understanding
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".
Deadline for manuscript submissions: closed (15 April 2022) | Viewed by 44347
Special Issue Editors
Interests: image processing; machine learning
Special Issues, Collections and Topics in MDPI journals
Interests: intelligent representation and calculation of geological information; geological environment monitoring and evaluation; geospatial information
Special Issues, Collections and Topics in MDPI journals
Interests: pattern recognition; machine learning; image processing; remote sensing
Special Issues, Collections and Topics in MDPI journals
Interests: signal processing; wireless communication; machine condition monitoring; biomedical signal processing; data analytics; machine learning; higher order statistics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Deep convolutional neural networks have been widely used in remote sensing image analysis and applications, e.g., classification, detection, regression, and inversion. Although these networks are somewhat successful in remote sensing image understanding, they still face a black-box problem, since both the feature extraction and classifier design are automatically learned. This problem seriously limits the development of deep learning and its applications in the field of remote sensing image understanding. In recent years, many explainable deep network models have been reported in the machine learning society, such as channel attention, spatial attention, self-attention, and non-local network. These networks, to some extent, promote the development of explainable deep learning and address some important problems in remote sensing image analysis. On the other hand, remote sensing applications usually involve several exact physical models, e.g., the radiance transfer model, linear unmixing model, and spatiotemporal autocorrelation, which can also effectively model the formation process from the remote sensing data to land-cover observation and environmental parameter monitoring. However, how to effectively integrate popular deep neural networks with the traditional remote sensing physical models is currently the main challenge in remote sensing image understanding. Therefore, the research of theoretically and physically explainable deep convolutional neural networks is currently one of the most popular topics and can offer important advantages in the applications of remote sensing image understanding. This Special Issue aims to publish high-quality research papers and salient and informative review articles addressing emerging trends in remote sensing image understanding using the combination of explainable deep network and remote sensing physical models. Original contributions, not currently under review in a journal or a conference, are solicited in relevant areas including, but not limited to, the following:
- Attention-aware deep convolutional neural networks for objection detection, segmentation, and recognition in remote sensing images.
- Non-local convolutional neural networks for remote sensing image applications.
- Compact deep network models for remote sensing image applications.
- Physical model integrating with deep convolutional neural networks for remote sensing image applications.
- Hybrid models of joining data-driven and model-driven for remote sensing image applications.
- Incorporating geographical laws into deep convolutional neural netowrks for remote sensing image applications.
- Review/Surveys of remote sensing image processing.
- New remote sensing image datasets.
Dr. Tao Lei
Dr. Tao Chen
Dr. Lefei Zhang
Prof. Dr. Asoke K. Nandi
Guest Editors
Manuscript Submission Information
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Keywords
- deep learning
- remote sensing image analysis
- land-cover observation
- Earth environmental monitoring
- attention mechanism
- data driven and model driven
- physical models
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