Project Summary
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Proposal Number:
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Project Title:Novel Neural Network Technology for Very Fast Analysis of Hyperspectral Imagery
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Small Business Concern:
Opto-Knowledge Systems, Inc. (OKSI)
1737 3rd Street
Manhattan Beach, CA 90266-6308
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Research Institution:
Jet Propulsion
Laboratory,
California Institute of Technology,
4800 Oak
Grove Drive,
Pasadena, CA 91101
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Principal Investigator/Project Manager: Nahum Gat, Ph.D.
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Technical Abstract:
Existing image analyses techniques, for spaceborne
multispectral imagery were developed based on the limited
number of spectral bands provided by sensors such as the
TM/MSS. These image interpretation methods are inadequate
for processing data provided by hyperspectral sensors to be
launched in the next few years, such as MODIS (36 non-
contiguous bands) and TRWIS (384 bands). The OKSI/JPL team
proposes to adapt and utilize novel advanced neural network
paradigms and architectures for solving such very high data
volume image interpretation problems. The proposed NN
learning algorithms have been demonstrated to (i) be faster
by orders of magnitude than conventional NN techniques, and
(ii) guarantee global optimization with respect to all the
NN parameters. These techniques apply to feedforward,
static and dynamic recurrent, ART or hybrid network
architectures, and can be used in supervised or unsupervised
training, thus making the application of NN to large data
volume hyperspectral image interpretation practical.
Hyperspectral data will be used during Phase I for proof of
principle demonstration and performance (speed and accuracy)
benchmarking. The team will work to develop value-added
data products based on EOS and other sensors' data.
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Potential Commecial Applications:
Applications include all the traditional remote sensing
areas but with much higher accuracy, resolution, and speed:
(i) agriculture: crop stress management due to heat,
drought, or storm damage; crop maturity analyses and
harvesting time optimization in high water value crops; high
spatial resolution fertilization and pesticide requirements
determination, (ii) environmental monitoring including
monitoring thermal pollution, superfund site cleanup
assessment, oils spills, wetlands, etc., (iii) forestry and
land management, and (iv) enhancement of the EOS science
applications in terrestrial, ocean, atmosphere, snow and ice
research. The use of the NN technology in the following
areas is also described: Photodiagnosis, environmental
monitoring, and military intelligence.
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