High Performance Computing in Remote Sensing - Chapter 4 pptx
... 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 Computing in Remote Sensing 50 45 40 35 30 25 Number ... unconstrained or nonnegatively constrained demixing. © 2008 by Taylor & Francis Group, LLC 94 High- Performance Computing in Remote Sen...
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... products are given in Table 14. 4. © 2008 by Taylor & Francis Group, LLC 3 54 High- Performance Computing in Remote Sensing TABLE 14. 5 Nominal Characteristics of an Earth Imaging Spectrometer for ... Geoscience and Remote Sensing, vol. 22, pp. 546 – 549 , 19 84. [2] A. S. Mazer, M. Martin, M. Lee, et al., Image-processing software for imaging spectrometry data-analysis....
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... 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 ... 60 Homogeneous 14 19 6 34 6 16 59 9 18 611 12 20 342 © 2008 by Taylor & Francis Group, LLC 36 High- Performance Computing in Remote Sensing strategies to inc...
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High Performance Computing in Remote Sensing - Chapter 7 potx
... Francis Group, LLC 148 High- Performance Computing in Remote Sensing 2562 241 9216012896 Number of CPUs 643 20 0 32 64 96 128 Speedup 160 192 Linear HeteroNEURAL HomoNEURAL 2 24 256 Figure 7.5 Scalability ... & Francis Group, LLC 142 High- Performance Computing in Remote Sensing experiments was Linux Fedora Core, and MPICH was the message-passing library used (...
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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-Cle...
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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 ... Group, LLC 200 High- Performance Computing in Remote Sensing [17] K. Ballinger et al. Basic Profile, v1.0. http://www.ws-i.org/Profiles/ BasicProfile-1. 0-2 00 4- 0 4- 1 6.h...
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High Performance Computing in Remote Sensing - Chapter 11 pdf
... running either from the command line or invoked by VISAT. © 2008 by Taylor & Francis Group, LLC 270 High- Performance Computing in Remote Sensing 908070605 040 3020100–10–20 40 –50–60–70–80–90 74 80 86 92 Altitude ... & Francis Group, LLC 250 High- Performance Computing in Remote Sensing duplication, and has increased the cooperation and coordination of efforts...
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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 ... 40 1 17.2.3 BSQ Format 40 1 17.3 Computer Simulation 40 2 17 .4 Practical Considerations 40 4 17 .4. 1 Algorithm Simplification Using R −1 40 4 17 .4. 2 Algorithm Implementation with Matr...
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High Performance Computing in Remote Sensing - Chapter 18 (end) pptx
... LLC 41 2 High- Performance Computing in Remote Sensing 18.5 Experimental Results 44 1 18.5.1 GPU Architectures 44 1 18.5.2 Hyperspectral Data 44 2 18.5.3 Performance Evaluation 44 3 18.6 Conclusions 44 9 18.7 ... & Francis Group, LLC 42 0 High- Performance Computing in Remote Sensing In summary, GPU makers will continue the battle for dominance in the co...
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Image Processing for Remote Sensing - Chapter 4 pptx
... 22 .44 8 3.06 5.39 5 .43 10.61 20.50 20.55 9 3.30 4. 82 4. 96 11.00 18.96 19.01 10 3.79 4. 38 4. 65 12.66 16.58 16.56 13 4. 13 4. 34 4. 54 14. 06 14. 99 15.08 16 4. 17 4. 31 4. 43 14. 95 14. 14 14. 12 17 4. 67 4. 19 ... 4. 82 11.00 18.96 10 3.79 4. 38 12.66 16.58 13 4. 13 4. 34 14. 06 14. 99 16 4. 17 4. 31 14. 95 14. 14 17 4. 67 4. 19 15.76 13.02 18 4. 94 4. 34 15....
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