Distributed Computing for Remotely Sensed Data Processing

Abstract

Remote sensing is an approach that collects information from a scene using airborne or spaceborne sensors. It has been widely used in various fields of earth observation and space exploration, including resource utilization, environmental monitoring, geological exploration, agricultural production, urban planning, and so on. Essentially, remote sensing technology can be recognized as multidisciplinary science and engineering to efficiently treat macro-observation issues. Remote sensing instruments have developed significantly during the last decade. Furthermore, the number of available sensors has increased significantly and their applications are much more widespread by remote sensing data being used in Google maps and social media applications in addition to more traditional environmental monitoring and land-use approaches. With the fast development of remote sensing techniques and platforms, the amount of data with higher spectral, spatial, temporal resolutions and multiple structures available from remote sensing systems is increasing at an extremely fast pace. This has posed serious challenges for efficient and scalable processing in a timely fashion to support various practical remote sensing applications [1]-[3].