High Performance Computing in Remote Sensing

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High Performance Computing in Remote Sensing. Advances in sensor technology are revolutionizing the way remotely sensed data are collected, managed, and analyzed. The incorporation of latest-generation sensors to airborne and satellite platforms is currently producing a nearly continual stream of high-dimensional data, and this explosion in the amount of collected information has rapidly created new processing challenges.. Giống các thư viện tài liệu khác được bạn đọc giới thiệu hoặc do tìm kiếm lại và giới thiệu lại cho các bạn với mục đích học tập , chúng tôi không thu phí từ bạn đọc ,nếu phát hiện nội dung phi phạm bản quyền hoặc vi phạm pháp luật xin thông báo cho chúng tôi,Ngoài tài liệu này, bạn có thể download Download tài liệu,đề thi,mẫu văn bản miễn phí phục vụ tham khảo Vài tài liệu tải về lỗi font chữ không xem được, thì do máy tính bạn không hỗ trợ font củ, bạn tải các font .vntime củ về cài sẽ xem được.

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High Performance Computing in Remote Sensing © 2008 by Taylor & Francis Group, LLC © 2008 by Taylor & Francis Group, LLC © 2008 by Taylor & Francis Group, LLC Chapman & Hall/CRC Taylor & Francis Group 6000 Broken Sound Parkway NW, Suite 300 Boca Raton, FL 33487-2742 © 2008 by Taylor & Francis Group, LLC Chapman & Hall/CRC is an imprint of Taylor & Francis Group, an Informa business No claim to original U.S. Government works Printed in the United States of America on acid-free paper 10 9 8 7 6 5 4 3 2 1 International Standard Book Number-13: 978-1-58488-662-4 (Hardcover) This book contains information obtained from authentic and highly regarded sources. Reprinted material is quoted with permission, and sources are indicated. A wide variety of references are listed. Reasonable efforts have been made to publish reliable data and information, but the author and the publisher cannot assume responsibility for the validity of all materials or for the conse-quences of their use. No part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers. For permission to photocopy or use material electronically from this work, please access www. copyright.com (http://www.copyright.com/) or contact the Copyright Clearance Center, Inc. (CCC) 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400. CCC is a not-for-profit organization that provides licenses and registration for a variety of users. For organizations that have been granted a photocopy license by the CCC, a separate system of payment has been arranged. Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. Library of Congress Cataloging-in-Publication Data High performance computing in remote sensing / Antonio J. Plaza and Chein-I Chang, editors. p. cm. -- (Chapman & Hall/CRC computer & information science series) Includes bibliographical references and index. ISBN 978-1-58488-662-4 (alk. paper) 1. High performance computing. 2. Remote sensing. I. Plaza, Antonio J. II. Chang, Chein-I. III. Title. IV. Series. QA76.88.H5277 2007 621.36’78028543--dc22 2007020736 Visit the Taylor & Francis Web site at http://www.taylorandfrancis.com and the CRC Press Web site at http://www.crcpress.com © 2008 by Taylor & Francis Group, LLC Contents 1 Introduction ............................................................ 1 Antonio Plaza and Chein-I Chang 2 High-Performance Computer Architectures for Remote Sensing Data Analysis: Overview and Case Study ..................................... 9 Antonio Plaza and Chein-I Chang 3 Computer Architectures for Multimedia and Video Analysis................43 Edmundo Saez, Jose Gonzalez-Mora, Nicolas Guil, Jose I. Benavides, and Emilio L. Zapata 4 Parallel Implementation of the ORASIS Algorithm for Remote Sensing Data Analysis ..................................................... 69 David Gillis and Jeffrey H. Bowles 5 Parallel Implementation of the Recursive Approximation of an Unsupervised Hierarchical Segmentation Algorithm .......................... 97 James C. Tilton 6 Computing for Analysis and Modeling of Hyperspectral Imagery..........109 Gregory P. Asner, Robert S. Haxo, and David E. Knapp 7 Parallel Implementation of Morphological Neural Networks for Hyperspectral Image Analysis.............................................131 Javier Plaza, Rosa Perez, Antonio Plaza, Pablo Martinez, and David Valencia 8 Parallel Wildland Fire Monitoring and Tracking Using Remotely Sensed Data ............................................................. 151 David Valencia, Pablo Martinez, Antonio Plaza, and Javier Plaza 9 An Introduction to Grids for Remote Sensing Applications................183 Craig A. Lee v © 2008 by Taylor & Francis Group, LLC ... - tailieumienphi.vn 658608