@InProceedings{GenovezFreSanBenLor:2017:OiSlCl,
author = "Genovez, Patricia Carneiro and Freitas, Corina da Costa and
Sant'Anna, Sidnei Jo{\~a}o Siqueira and Bentz, Cristina Maria and
Lorenzzetti, Jo{\~a}o Ant{\^o}nio",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {} and {}
and {} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
title = "Oil Slicks Classification using Multivariate Statistical Modelling
Applied to SAR and PolSAR Data",
booktitle = "Anais...",
year = "2017",
editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz
Eduardo Oliveira e Cruz de",
pages = "6764--6771",
organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 18. (SBSR)",
publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
address = "S{\~a}o Jos{\'e} dos Campos",
abstract = "Polarimetric Synthetic Aperture Radars (PolSAR) have been used to
detect oil slicks at sea surface. The numerous platforms
available, acquiring data in different formats and configurations,
pose as a challenge to understand which is the better format and
statistical modeling to improve the oil detection. To contribute
with this issue, a combination of different data formats in single
look complex, intensity and amplitude, with full and dual
polarimetric channels, were evaluated considering adequate
statistical modeling to classify each data type. The better
results were obtained by the full and dual-pol matrices, however
when the HV channel is excluded the accuracy levels are damaged.
Therefore, it is better use the data in intensity or amplitude
preserving the HV channel, than use a polarimetric data without
this channel. The classifier demonstrated potential to detect the
three types of oils released, being more effective in detecting
biogenic oils rather than mineral oils. The uncertainty levels
increase from the center to the border of the mineral oil slicks,
indicating the presence of transition regions, possibly related to
different weathering mechanisms. Future studies should be done
including more SAR images, with known occurrences and field data
to investigate properly the trade-offs related with each data
format to discriminate different oil types.",
conference-location = "Santos",
conference-year = "28-31 maio 2017",
isbn = "978-85-17-00088-1",
label = "59356",
language = "en",
organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
ibi = "8JMKD3MGP6W34M/3PSMDG8",
url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSMDG8",
targetfile = "59356.pdf",
type = "Sensoriamento remoto de microondas",
urlaccessdate = "27 abr. 2024"
}