Comparative Sensor Fusion Between Hyperspectral and Multispectral Satellite Sensors for Monitoring Microcystin Distribution in Lake Erie

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  author =       "Ni-Bin Chang and Benjamin Vannah and Y. Jeffrey Yang",
  journal =      "IEEE Journal of Selected Topics in Applied Earth
                 Observations and Remote Sensing",
  title =        "Comparative Sensor Fusion Between Hyperspectral and
                 Multispectral Satellite Sensors for Monitoring
                 Microcystin Distribution in Lake Erie",
  year =         "2014",
  month =        jun,
  volume =       "7",
  number =       "6",
  pages =        "2426--2442",
  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.",
  keywords =     "genetic algorithms, genetic programming, Harmful algal
                 bloom, image fusion, machine learning, microcystin,
                 remote sensing",
  DOI =          "doi:10.1109/JSTARS.2014.2329913",
  ISSN =         "1939-1404",
  notes =        "Also known as \cite{6851120}",

Genetic Programming entries for Ni-Bin Chang Benjamin W Vannah Y Jeffrey Yang