High Performance Computing in Remote Sensing

High Performance Computing in Remote Sensing

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Features

  • Provides a state-of-the-art look at the HPC techniques—including cluster computers, workstation and grid networks, field programmable gate arrays (FPGAs), and graphics processing units (GPUs)—used to solve remote sensing problems
  • Presents well-established processing techniques for data acquisition, calibration, correction, target detection/classification, image segmentation, model inversion, and visualization
  • Includes several chapters that focus on hyperspectral imaging
  • Features contributors from world-renowned universities, NASA, the European Space Agency, and the Aerospace Corporation, providing insight into novel developments and realistic applications in the areas of security and defense, computer engineering, and Earth, space, and environmental sciences
  • Contains more than 200 high-quality figures and tables to promote a better understanding of the concepts described
  • Summary

    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.

    Table of 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

    Editorial Reviews

    ". . . 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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