High Performance Computing in Remote Sensing
Coordonnateurs : Plaza Antonio J., Chang Chein-I
Solutions for Time-Critical Remote Sensing Applications
The recent use of latest-generation sensors in airborne and satellite platforms is producing a nearly continual stream of high-dimensional data, which, in turn, is creating new processing challenges. To address the computational requirements of time-critical applications, researchers have begun incorporating high performance computing (HPC) models in remote sensing missions. High Performance Computing in Remote Sensing is one of the first volumes to explore state-of-the-art HPC techniques in the context of remote sensing problems. It focuses on the computational complexity of algorithms that are designed for parallel computing and processing.
A Diverse Collection of Parallel Computing Techniques and Architectures
The book first addresses key computing concepts and developments in remote sensing. It also covers application areas not necessarily related to remote sensing, such as multimedia and video processing. Each subsequent chapter illustrates a specific parallel computing paradigm, including multiprocessor (cluster-based) systems, large-scale and heterogeneous networks of computers, grid computing platforms, and specialized hardware architectures for remotely sensed data analysis and interpretation.
An Interdisciplinary Forum to Encourage Novel Ideas
The extensive reviews of current and future developments combined with thoughtful perspectives on the potential challenges of adapting HPC paradigms to remote sensing problems will undoubtedly foster collaboration and development among many fields.
Date de parution : 09-2019
15.6x23.4 cm
Date de parution : 11-2007
Ouvrage de 448 p.
15.6x23.4 cm
Thèmes de High Performance Computing in Remote Sensing :
Mots-clés :
Hyperspectral Imaging; Pixel Vector; Hyperspectral Data; High Performance Computing; Grid Services; Execution Time; CEM; Data Sets; Remote Sensing; Hyperspectral Image Analysis; Hyperspectral Data Sets; Imaging Spectrometer; Grid Computing Environments; AVIRIS Data Set; Reconfigurable Computing; Hyperspectral Image Processing; PPI; Endmember Extraction Algorithm; Systolic Array; Endmember Extraction; Imaging Spectroscopy; Remote Sensing Data; CORDIC; NASA’s Goddard Space Flight; Remote Sensing Problems