State of the Art: High-Performance and High-Throughput Computing for Remote Sensing Big Data

Abstract

In recent years, with the increasing number of Earth observation satellites and the popularization and application of various sensors, remote sensing data have shown a rapid growth trend and present typical big data characteristics. The continuous enrichment of remote sensing data has provided large information resources for Earth science research and promoted the wide application of remote sensing technology in resources, ecology, environment, energy, health, urban management, and so on. However, mining information from multisource heterogeneous remote sensing big data, which requires a large amount of computing power, has many challenges in terms of generality, security, and timeliness. In this article, we summarize the existing research on high-performance computing (HPC) and high-throughput computing (HTC) technologies toward improving the processing efficiency of remote sensing big data. We also analyze the problems and challenges of HPC/HTC technologies in the storage, computation, and analysis of remote sensing big data. Finally, we predict the trend of remote sensing big data processing in the direction of HPC/HTC.