High Performance Computing in Remote Sensing
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 inco...
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CRC Press
2009
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| Truy cập trực tuyến: | http://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/1013 |
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| Thư viện lưu trữ: | Thư viện Trường Đại học Đà Lạt |
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oai:scholar.dlu.edu.vn:DLU123456789-1013 |
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Thư viện Trường Đại học Đà Lạt |
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English |
| topic |
Environmental Sciences |
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Environmental Sciences Plaza, Antonio Chang, Chein-I High Performance Computing in Remote Sensing |
| description |
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. |
| format |
Book |
| author |
Plaza, Antonio Chang, Chein-I |
| author_facet |
Plaza, Antonio Chang, Chein-I |
| author_sort |
Plaza, Antonio |
| title |
High Performance Computing in Remote Sensing |
| title_short |
High Performance Computing in Remote Sensing |
| title_full |
High Performance Computing in Remote Sensing |
| title_fullStr |
High Performance Computing in Remote Sensing |
| title_full_unstemmed |
High Performance Computing in Remote Sensing |
| title_sort |
high performance computing in remote sensing |
| publisher |
CRC Press |
| publishDate |
2009 |
| url |
http://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/1013 |
| _version_ |
1757668987788853248 |
| spelling |
oai:scholar.dlu.edu.vn:DLU123456789-10132009-10-12T07:24:33Z High Performance Computing in Remote Sensing Plaza, Antonio Chang, Chein-I Environmental Sciences 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 Index 2009-10-12T07:24:33Z 2009-10-12T07:24:33Z 2007 Book 978158488662 http://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/1013 en application/octet-stream CRC Press |