Comparative Sensor Fusion Between Hyperspectral and Multispectral Satellite Sensors for Monitoring Microcystin Distribution in Lake Erie
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- @Article{Chang:2014:ieeeSTAEORS,
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author = "Ni-Bin Chang and Benjamin Vannah and Y. Jeffrey Yang",
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journal = "IEEE Journal of Selected Topics in Applied Earth
Observations and Remote Sensing",
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title = "Comparative Sensor Fusion Between Hyperspectral and
Multispectral Satellite Sensors for Monitoring
Microcystin Distribution in Lake Erie",
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year = "2014",
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month = jun,
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volume = "7",
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number = "6",
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pages = "2426--2442",
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abstract = "Urban growth and agricultural production have caused
an influx of nutrients into Lake Erie, leading to
eutrophication in the water body. These conditions
result in the formation of algal blooms, some of which
are toxic due to the presence of Microcystis (a
cyanobacteria), which produces the hepatotoxin
microcystin. The hepatotoxin microcystin threatens
human health and the ecosystem, and it is a concern for
water treatment plants using the lake water as a tap
water source. This study demonstrates the prototype of
a near real-time early warning system using integrated
data fusion and mining (IDFM) techniques with the aid
of both hyperspectral (MERIS) and multispectral (MODIS
and Landsat) satellite sensors to determine
spatiotemporal microcystin concentrations in Lake Erie.
In the proposed IDFM, the MODIS images with high
temporal resolution are fused with the MERIS and
Landsat images with higher spatial resolution to create
synthetic images on a daily basis. The spatiotemporal
distributions of microcystin within western Lake Erie
were then reconstructed using the band data from the
fused products with machine learning or data mining
techniques such as genetic programming (GP) models. The
performance of the data mining models derived using
fused hyperspectral and fused multispectral sensor data
are quantified using four statistical indices. These
data mining models were further compared with
traditional two-band models in terms of microcystin
prediction accuracy. This study confirmed that GP
models outperformed traditional two-band models, and
additional spectral reflectance data offered by
hyperspectral sensors produces a noticeable increase in
the prediction accuracy especially in the range of low
microcystin concentrations.",
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keywords = "genetic algorithms, genetic programming, Harmful algal
bloom, image fusion, machine learning, microcystin,
remote sensing",
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DOI = "doi:10.1109/JSTARS.2014.2329913",
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ISSN = "1939-1404",
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notes = "Also known as \cite{6851120}",
- }
Genetic Programming entries for
Ni-Bin Chang
Benjamin W Vannah
Y Jeffrey Yang
Citations