High Performance Computing in Remote Sensing - Chapter 9 pps
High Performance Computing in Remote Sensing - Chapter 9 pps
... of peer-to-peer computing, Internet computing, high- throughput computing, distributed supercomputing, or ubiquitous computing. 9. 4.3 End-User Tools Thus far, we have described the service-oriented ... Monitoring and Control © 2008 by Taylor & Francis Group, LLC 184 High- Performance Computing in Remote Sensing 9. 1 Introduction The concept of distributed computing ha...
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High Performance Computing in Remote Sensing - Chapter 8 pps
... and BareSoil Buildings Concrete/Asphalt Corn Corn? Corn-EW Corn-NS Corn-CleanTill Corn-CleanTill-EW Corn-CleanTill-NS Corn-CleanTill-NS-Irrigated Corn-CleanTilled-NS? Corn-MinTill Corn-MinTill-EW Corn-MinTill-NS Corn-NoTill Corn-NoTill-EW Corn-NoTill-NS Fescue Grass Grass/Trees Grass/Pasture-mowed Grass/Pasture Grass-runway Hay Hay? Hay-Alfalfa Lake NotCropped Oats Figure ... 71.64 Corn-Clean...
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High Performance Computing in Remote Sensing - Chapter 2 docx
... 12 High- Performance Computing in Remote Sensing is a good example of the computational requirements introduced by remote sensing applications, there are many other remote sensing areas in which ... WTC0 1-2 8 0.0 69 0.0 69 0.0 69 0.0 69 Concrete – WTC0 1-3 7Am 0.073 0.073 0.075 0.073 Concrete – WTC0 1-3 7B 0. 090 0. 090 0. 090 0. 090 © 2008 by Taylor & Fra...
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High Performance Computing in Remote Sensing - Chapter 4 pptx
... unconstrained or nonnegatively constrained demixing. © 2008 by Taylor & Francis Group, LLC 94 High- Performance Computing in Remote Sensing many gigabytes in size). ORASIS, like most linear mixing ... sum-to-one and non- negativity constraints (Eqs. 4.2 and 4.3, respectively) are enforced, then the linear © 2008 by Taylor & Francis Group, LLC 78 High- Performance Com...
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High Performance Computing in Remote Sensing - Chapter 7 potx
... PCT-Based Morphological Class Label Information ( 298 1) Features (3256) Features (36 79) Fallow rough plow 96 .51 91 .90 96 .78 Fallow smooth 93 .72 93 .21 97 .63 Stubble 94 .71 95 .43 98 .96 Celery 89. 34 ... by Taylor & Francis Group, LLC 148 High- Performance Computing in Remote Sensing 256224 192 16012 896 Number of CPUs 64320 0 32 64 96 128 Speedup 160 192...
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High Performance Computing in Remote Sensing - Chapter 11 pdf
... European-wide service grid © 2008 by Taylor & Francis Group, LLC 240 High- Performance Computing in Remote Sensing 11.2.1 ERS-2 The ERS-2 Earth Observation mission [3] has been operating since 199 5. ... running either from the command line or invoked by VISAT. © 2008 by Taylor & Francis Group, LLC 270 High- Performance Computing in Remote Sensing 90 80706050403...
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High Performance Computing in Remote Sensing - Chapter 14 pptx
... Francis Group, LLC 358 High- Performance Computing in Remote Sensing [ 19] D. A. Roberts, M. Gardner, R. Church, et al., Mapping chaparral in the Santa Monica Mountains usingmultipleendmember spectral ... tool for remote- sensing. IEEE Transactions on Geoscience and Remote Sensing, vol. 22, pp. 546–5 49, 198 4. [2] A. S. Mazer, M. Martin, M. Lee, et al., Image-processing sof...
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High Performance Computing in Remote Sensing - Chapter 17 docx
... real-time processing fashions will be discussed to fit the three remote sensing data formats: pixel-by-pixel processing for BIP formats, line-by-line processing for BIL formats, and band-by-band ... until the resulting LSE is below a prescribed error threshold η. © 2008 by Taylor & Francis Group, LLC 398 High- Performance Computing in Remote Sensing detection and discrimina...
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High Performance Computing in Remote Sensing - Chapter 18 (end) pptx
... LLC 420 High- Performance Computing in Remote Sensing In summary, GPU makers will continue the battle for dominance in the consumer gaming industry, producing a competitive environment with rapid innovation ... in Listing 3) . © 2008 by Taylor & Francis Group, LLC 422 High- Performance Computing in Remote Sensing Distribution converts loop-independent and forwa...
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Environmental Modelling with GIs and Remote Sensing - Chapter 9 pps
... NDVI - 0. 39 (Lind and Fensholt 199 9) fAPAR = 1.41 * NDVI- 0.40, r2 = 0 .96 3 (Pinter 199 2) fAPAR = 1.67 * NDVI - 0.08 (Prince and Goward 199 5) fAPAR = 1.62 * NDVI - 0.04, ... Geography, 199 9,2,4 3-5 6. Lindroth, A., 198 4, Seasonal variation in pine forest evaporation and canopy conductance. Avhandling 758, Uppsala Universitet. Lindstrom, G.,...
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