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.
Preface by Antonio J. Plaza and Chein-I Chang
High Performance Computing Architectures for Remote Sensing Data Analysis: Overview and Case Study by Antonio J. Plaza and Chein-I Chang
Computer Architectures for Multimedia and Video Analysis by Edmundo Sáez, José González-Mora, Nicolás Guil, José I. Benavides, and Emilio L. Zapata
Parallel Implementation of the ORASIS Algorithm for Remote Sensing Data Analysis by David Gillis and Jeffrey H. Bowles
Parallel Implementation of the Recursive Approximation of an Unsupervised Hierarchical Segmentation Algorithm by James C. Tilton
Computing for Analysis and Modeling of Hyperspectral Imagery by Gregory P. Asner, Robert S. Haxo, and David E. Knapp
Parallel Implementation of Morphological Neural Networks for Hyperspectral Image Analysis by Javier Plaza, Rosa Pérez, Antonio J. Plaza, Pablo Martínez, and David Valencia
Parallel Wildland Fire Monitoring and Tracking Using Remotely Sensed Data by David Valencia, Pablo Martínez, Antonio J. Plaza, and Javier Plaza
An Introduction to Grids for Remote Sensing Applications by Craig A. Lee
Remote Sensing Grids: Architecture and Implementation by Samuel D. Gasster, Craig A. Lee, and James W. Palko
Open Grid Services for Envisat and Earth Observation Applications by Luigi Fusco, Roberto Cossu, and Christian Retscher
Design and Implementation of a Grid Computing Environment for Remote Sensing by Giovanni Aloisio, Massimo Cafaro, Italo Epicoco, Gianvito Quarta, and Sandro Fiore
A Solutionware for Hyperspectral Image Processing and Analysis by Miguel Vélez-Reyes, Wilson Rivera-Gallego, and Luis O. Jiménez-Rodríguez
AVIRIS and Related 21st-Century Imaging Spectrometers for Earth and Space Science by Robert O. Green
Remote Sensing and High Performance Reconfigurable Computing Systems by Esam El-Araby, Mohamed Taher, Tarek El-Ghazawi, and Jacqueline Le Moigne
FPGA Design for Real-Time Implementation of Constrained Energy Minimization for Hyperspectral Target Detection by Jianwei Wang and Chein-I Chang
Real-Time Online Processing of Hyperspectral Imagery for Target Detection and Discrimination by Qian Du
Real-Time On-Board Hyperspectral Image Processing Using Programmable Graphics Hardware by Javier Setoain, Manuel Prieto, Christian Tenllado, and Francisco Tirado
". . . provides a state-of-the-art summary of the HPC techniques—including cluster computers, workstation and grid networks, field programmable gate arrays, and graphics processing units—used to solve remote sensing problems. It is a good reference for researchers and practitioners in remote sensing, computer engineering, and other related fields. The book is especially useful for design and implementation of high performance systems for collecting, storing, and analyzing hyperspectral remotely sensed data."
– In Photogrammetric Engineering & Remote Sensing, January 2009
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