High Performance Computing in Remote Sensing

Description

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.

Contents

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

Index

Related Subjects

1. Remote Sensing & Photogrammetry
2. Algorithms and Complexity
3. Signal Processing
4. Algorithms & Complexity
5. Digital Signal Processing