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...
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...
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...
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...
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...
High Performance Computing in Remote Sensing - Chapter 11 pdf
... European-wide service grid
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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...
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...
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...
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...
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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