| 000 | 01971nam a2200217 a 4500 | ||
|---|---|---|---|
| 001 | 00025016 | ||
| 008 | 20425s2011 xxu eng d | ||
| 020 | _a3845406186 (paperback) | ||
| 020 | _a9783845406183 (paperback) | ||
| 082 | 0 | 0 |
_550 _222 |
| 100 | 1 | _aTesfamariam, Ermias Beyene. | |
| 245 | 1 | 0 |
_aDistributed processing of large remote sensing images using mapreduce : _b a case of edge detection methods / _cErmias Beyene Tesfamariam. |
| 260 |
_a[S.l.] : _bLAP LAMBERT Academic Publishing, _c2011. |
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| 300 |
_a84 p. ; _c22 cm. |
||
| 520 | _aAdvances 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 |
_aEarth sciences _xRemote sensing. |
|
| 650 | 0 | _aRemote-sensing images. | |
| 999 |
_c12095 _d12095 |
||
| 952 |
_w2012-04-25 _p3010025016 _r2012-04-25 _40 _ekarim International _00 _bBRACUL _10 _o550 TES _d2012-04-25 _t1 _70 _cGEN _2ddc _g5500.00 _yBK _aBRACUL |
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