Distributed processing of large remote sensing images using mapreduce : (Record no. 12095)

MARC details
000 -LEADER
fixed length control field 01971nam a2200217 a 4500
001 - CONTROL NUMBER
control field 00025016
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 20425s2011 xxu eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 3845406186 (paperback)
International Standard Book Number 9783845406183 (paperback)
082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER
-- 50
Edition number 22
999 ## - SYSTEM CONTROL NUMBERS (KOHA)
Koha biblionumber 12095
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Tesfamariam, Ermias Beyene.
245 10 - TITLE STATEMENT
Title Distributed processing of large remote sensing images using mapreduce :
Remainder of title a case of edge detection methods /
Statement of responsibility, etc Ermias Beyene Tesfamariam.
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc [S.l.] :
Name of publisher, distributor, etc LAP LAMBERT Academic Publishing,
Date of publication, distribution, etc 2011.
300 ## - PHYSICAL DESCRIPTION
Extent 84 p. ;
Dimensions 22 cm.
520 ## - SUMMARY, ETC.
Summary, etc Advances in remote sensing technology and their ever increasing repositories of the collected data are revolutionizing the mechanisms these data are collected, stored and processed. This exponential growth of data archives and the increasing users’ demand for real-and near-real time remote sensing data products has challenged the data providers to deliver the required services. The remote sensing community has recognized the challenge in processing large and complex satellite datasets to derive customized products and several efforts have been made in the past few years towards incorporation of high-performance computing models. This study analyzes the recent advancements in distributed computing technologies, the MapReduce programming model, extends it for use in the area of remote sensing image processing. Performance tests for processing of large archives of Landsat images were performed with the Hadoop framework. The findings demonstrate that MapReduce has a potential for scaling large-scale remotely sensed images processing and perform more complex geospatial problems.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Earth sciences
General subdivision Remote sensing.
Topical term or geographic name as entry element Remote-sensing images.
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Shelving location Date acquired Source of acquisition Cost, normal purchase price Total Checkouts Total Renewals Full call number Barcode Date last seen Date checked out Copy number Price effective from Koha item type
    Dewey Decimal Classification     Ayesha Abed Library Ayesha Abed Library General Stacks 25/04/2012 karim International 5500.00 2 6 550 TES 3010025016 03/09/2022 30/08/2022 1 25/04/2012 Book