Cloud detection of MODIS multispectral images (Articolo in rivista)

Type
Label
  • Cloud detection of MODIS multispectral images (Articolo in rivista) (literal)
Anno
  • 2014-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1175/JTECH-D-13-00088.1 (literal)
Alternative label
  • Loredana Murino; Umberto Amato; Maria Francesca Carfora; Anestis Antoniadis; Bormin Huang; W. Paul Menzel, Ph.D.; Carmine Serio (2014)
    Cloud detection of MODIS multispectral images
    in Journal of atmospheric and oceanic technology; American Meteorological Society, Boston (Stati Uniti d'America)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Loredana Murino; Umberto Amato; Maria Francesca Carfora; Anestis Antoniadis; Bormin Huang; W. Paul Menzel, Ph.D.; Carmine Serio (literal)
Pagina inizio
  • 347 (literal)
Pagina fine
  • 365 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 31 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
  • 2 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Istituto per le Applicazioni del Calcolo 'Mauro Picone' CNR, Sede di Napoli, Italy Laboratoire Jean Kuntzmann, Universit ? Joseph Fourier, Grenoble, France Space Science and Engineering Center (SSEC), University of Wisconsin, Madison DIFA Università della Basilicata (literal)
Titolo
  • Cloud detection of MODIS multispectral images (literal)
Abstract
  • Methods coming from statistics and pattern recognition to estimate the cloud mask from radiance measured by visible and infrared sensors on board satellites are gaining greater consideration for their ability to properly exploit the increasing number of channels available with current and next-generation sensors. Endowed with physical arguments, they give rise to robust methods for accurately estimating the cloud mask. Application of such classification methods to Moderate Resolution Imaging Spectroradiometer (MODIS) data is discussed in this paper. Three different types of MODIS datasets are considered: synthetic (radiance is simulated by proper radiative transfer models); annotated (real MODIS data labeled by a meteorologist as clear or cloudy); and real MODIS data, whose truth is obtained from the official MODIS cloud mask product. A full assessment of the MODIS spectral bands is performed, aimed at understanding the role of the spectral bands in detecting clouds and at achieving top performance with very few properly chosen spectral channels. Local methods that use spatial correlation of images to improve classification, reducing the pseudonuisance of nonlocal methods, have also been tested on real data. (literal)
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