Special Issue "Deep Learning for Remote Sensing Data"
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".
Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 28130
Special Issue Editors
Interests: deep learning; remote sensing; hyperspectral image analysis; classification; tracking; data fusion; video analysis; 3D point cloud analysis; LiDAR data analysis
Interests: pattern recognition; computer vision and spectral imaging with their applications to remote sensing and environmental informatics
Special Issues, Collections and Topics in MDPI journals
Interests: deep learning; remote sensing; hyperspectral image analysis; adversarial attacks and defenses

2. Institute of Advanced Research in Artificial Intelligence (IARAI), 1030 Vienna, Austria
Interests: machine (deep) learning; image and signal processing; multisensor data fusion
Special Issues, Collections and Topics in MDPI journals
Interests: reflectance models; pattern recognition; machine learning; computer vision; segmentation; graph-matching; imaging spectroscopy; shape-from-X; environmental management
Special Issue Information
The past decade has seen a quantum leap in the accuracies of numerous signal and image processing tasks due to deep learning. Deep learning can model very complex nonlinear mathematical functions in a data-driven manner, which makes it an attractive technology for numerous tasks in the field of remote sensing. Moreover, the recent rise in the number of Earth-observing satellites has also resulted in large volumes of data, which makes the application of deep learning even more appealing for remote sensing data. The ever-increasing computational capacity of GPUs and efficient implementation of deep learning algorithms in public software libraries are additional factors that are currently shifting the focus of the remote sensing community towards deep learning as the main data analysis tool.
This Special Issue on “Deep Learning for Remote Sensing Data” aims to capture recent advances and trends in exploiting deep learning for complex remote sensing data analysis tasks. The Special Issue welcomes contributions towards both theoretical advancements of the deep learning framework in the context of remote sensing, as well as application of this technology to remote sensing data. The topics of interest include but are not limited to:
- Deep learning for remote sensing image processing, e.g., pan-sharpening, super-resolution;
- Remote sensing data analysis with deep learning;
- Specialized network architectures and deep learning algorithms for remote sensing data;
- Transfer learning and cross-domain learning;
- Real and synthetic remote sensing data generation;
- Multimodality data fusion with deep models;
- Pixel-level and subpixel-level classification, e.g., hyperspectral unmixing, segmentation.
Keywords
- remote sensing
- deep learning
- hyperspectral imaging
- segmentation
- pan-sharpening
- hyperspectral unmixing