The volume of synthetic aperture radar (SAR) data is growing at ever-increasing rates thanks to the lunch of numerous radar satellites that can image the earth under high resolution, multi-polarized and wide swath mode at short repeat cycles. Such big SAR data, on the one hand, challenge the conventional processing routines and present opportunities for data-driven analysis technologies (e.g., machine learning), on the other hand, enable extraction of information at ultra-fine or extremely large scale, allowing for innovative applications in the fields, e.g., seismology, geology, hydrology, and ecology. In the era of big SAR data, more and more researchers are addressing methods for exploiting value-added information that are usually aided by artificial intelligence technologies and new insights into the dynamic earth by discovering uncovered information. This Research Topic aims to promote a deeper understanding of these topics and serve as a platform for exchanging ideas among researchers from different disciplines.
Big SAR data pose great challenges in terms of management, processing and analytical modeling. How can such challenges be tackled with artificial intelligence technologies, such as machine learning, deep learning, and natural language processing? What strategy can be adopted in the data ocean for applications with a high demand for computing efficiency? Recent research has addressed these problems to some extent. For example, deep learning algorithms have been applied in SAR despeckling, classification, target identification, and phase unwrapping. High performance parallel computing has been applied for SAR data processing. More impressively, Google Earth Engine (GEE) has become a very successful platform for SAR data processing, analysis and visualization. We are desired to see innovative algorithms and exciting applications related to big SAR data in this Research Topic. The contributions from the researchers are believed to help us better understand to what extend the big SAR data can be turned into big insight.
This Research Topic calls for papers that deal with innovative SAR/InSAR data processing and analysis techniques and applications. Potential topics of interest include, but are not limited to the following:
• Advanced SAR/InSAR/PolSAR/PolInSAR/TomoSAR processing techniques
• Intelligent fusion of SAR and other earth observation data
• High performance computing and cloud services for big SAR data
• Big SAR data mining and visualization
• Insights into dynamic earth with big SAR data
The volume of synthetic aperture radar (SAR) data is growing at ever-increasing rates thanks to the lunch of numerous radar satellites that can image the earth under high resolution, multi-polarized and wide swath mode at short repeat cycles. Such big SAR data, on the one hand, challenge the conventional processing routines and present opportunities for data-driven analysis technologies (e.g., machine learning), on the other hand, enable extraction of information at ultra-fine or extremely large scale, allowing for innovative applications in the fields, e.g., seismology, geology, hydrology, and ecology. In the era of big SAR data, more and more researchers are addressing methods for exploiting value-added information that are usually aided by artificial intelligence technologies and new insights into the dynamic earth by discovering uncovered information. This Research Topic aims to promote a deeper understanding of these topics and serve as a platform for exchanging ideas among researchers from different disciplines.
Big SAR data pose great challenges in terms of management, processing and analytical modeling. How can such challenges be tackled with artificial intelligence technologies, such as machine learning, deep learning, and natural language processing? What strategy can be adopted in the data ocean for applications with a high demand for computing efficiency? Recent research has addressed these problems to some extent. For example, deep learning algorithms have been applied in SAR despeckling, classification, target identification, and phase unwrapping. High performance parallel computing has been applied for SAR data processing. More impressively, Google Earth Engine (GEE) has become a very successful platform for SAR data processing, analysis and visualization. We are desired to see innovative algorithms and exciting applications related to big SAR data in this Research Topic. The contributions from the researchers are believed to help us better understand to what extend the big SAR data can be turned into big insight.
This Research Topic calls for papers that deal with innovative SAR/InSAR data processing and analysis techniques and applications. Potential topics of interest include, but are not limited to the following:
• Advanced SAR/InSAR/PolSAR/PolInSAR/TomoSAR processing techniques
• Intelligent fusion of SAR and other earth observation data
• High performance computing and cloud services for big SAR data
• Big SAR data mining and visualization
• Insights into dynamic earth with big SAR data