Special Issue "Deep Learning for Remote Sensing Data"
Deadline for manuscript submissions: 25 June 2021.
Interests: deep learning; remote sensing; hyperspectral image analysis; classification; tracking; data fusion; video analysis; 3D point cloud analysis; LiDAR data analysis
Interests: hyperspectral image preprocessing; hyperspectral image visualization; spectral–spatial feature extraction; hyperspectral unmixing; hyperspectral object detection and classification; 3D modeling and depth estimation; multimodal image fusion; applications in environmental monitoring; agriculture and medicine
Interests: deep learning; remote sensing; hyperspectral image analysis; adversarial attacks and defenses
2. CTO and co-founder at VasoGnosis, 313 N Plankinton Ave, Suite 211, Milwaukee, WI 53203, USA
Interests: multisensor data fusion; machine and deep learning; image and signal processing; hyperspectral image analysis
Special Issues and Collections in MDPI journals
Special Issue in ISPRS International Journal of Geo-Information: Data Mining and Feature Extraction from Satellite Images and Point Cloud Data
Special Issue in Remote Sensing: Advances in Earth Observations Analytics: Leveraging Radar and Optical Together
Special Issue in Remote Sensing: Advanced Multisensor Image Analysis Techniques for Land-Cover Mapping
Special Issue in Remote Sensing: Deep Learning and Feature Mining Using Hyperspectral Imagery
Special Issue in Remote Sensing: Image Processing and Spatial Neighbourhoods for Remote Sensing Data Analysis
Special Issue in Remote Sensing: Remote Sensing Applications of Image Denoising and Restoration
Special Issue in Algorithms: Feature Papers in Evolutionary Algorithms and Machine Learning
Special Issue in Remote Sensing: Spectral-Spatial Segmentation and Classification of Remotely Sensed Hyperspectral Images
Interests: reflectance models, pattern recognition, machine learning, computer vision, segmentation, graph-matching, imaging spectroscopy, shape-from-X, environmental management
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.
- remote sensing
- deep learning
- hyperspectral imaging
- hyperspectral unmixing