High-Performance Computing in Geoscience and Remote Sensing
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".
Deadline for manuscript submissions: closed (20 September 2018) | Viewed by 26777
Special Issue Editors
Interests: signal processing and pattern recognition; automated target detection; image fusion; image information mining
Special Issues, Collections and Topics in MDPI journals
Interests: hyperspectral imagery; remote sensing; intelligent processing; machine learning; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Interests: hyperspectral image processing; remote sensing big data processing; parallel computing; machine learning; cloud computing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Recent advances in remote sensors with higher spectral, spatial, and temporal resolutions have significantly increased data volumes, which pose a challenge to process and analyze the resulting massive data in a timely fashion to support practical applications. Meanwhile, the development of computationally demanding deep learning techniques in data analytics also requires high performance computing. Therefore, parallel, distributed, and grid computing facilities and techniques have become indispensable tools in geoscience and remote sensing. In recent years, high-performance computing facilities and techniques have been dramatically advanced. For instance, the popular graphics processing unit (GPU) has evolved into a highly parallel many-core processor with tremendous computing power and high memory bandwidth to offer two to three orders of magnitude speedup over the CPU. This Special Issue of Sensors aims to publish the recent advances in utilizing newly high-performance computing facilities to expedite the processing and analysis of geoscience and remote sensing data for various applications. Papers are solicited in, but not limited to, the following areas:
- High performance computing for optical, microwave, and lidar remote sensing data processing and analysis.
- High performance computing for spaceborne, airborne, and UAV platforms.
- High performance computing for on-board processing.
- Recent development of high performance computing solutions for machine learning, artificial intelligence, deep learning, and big data analytics.
Dr. Nicolas Younan
Dr. Qian Du
Dr. Zebin Wu
Guest Editors
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Keywords
- High performance computing
- Parallel, distributed, and grid computing
- Large-scale data processing
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