@Article{GenovezEbeFreBenFre:2017:InHySy,
author = "Genovez, Patr{\'{\i}}cia Carneiro and Ebecken, Nelson and
Freitas, Corina da Costa and Bentz, Cristina Maria and Freitas,
Ramon",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and
{Universidade Federal do Rio de Janeiro (UFRJ)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Centro de Pesquisa da
Petrobr{\'a}s (CENPES)} and {Camargo-Schubert Wind Engineering}",
title = "Intelligent hybrid system for dark spot detection using SAR data",
journal = "Expert Systems with Applications",
year = "2017",
volume = "81",
pages = "384--397",
month = "Sept.",
keywords = "Cluster analysis, Computational Intelligence, Digital Image
Processing, Feature selection, Oil spills detection, Synthetic
Aperture Radar.",
abstract = "Synthetic Aperture Radars (SAR) are the main instrument used to
support oil detection systems. In the microwave spectrum, oil
slicks are identified as dark spots, regions with low backscatter
at sea surface. Automatic and semi-automatic systems were
developed to minimize processing time, the occurrence of false
alarms and the subjectivity of human interpretation. This study
presents an intelligent hybrid system, which integrates automatic
and semi-automatic procedures to detect dark spots, in six steps:
(I) SAR pre-processing; (II) Image segmentation; (III) Feature
extraction and selection; (IV) Automatic clustering analysis; (V)
Decision rules and, if needed; (VI) Semi-automatic processing. The
results proved that the feature selection is essential to improve
the detection capability, keeping only five pattern features to
automate the clustering procedure. The semi-automatic method gave
back more accurate geometries. The automatic approach erred more
including regions, increasing the dark spots area, while the
semi-automatic method erred more excluding regions. For
well-defined and contrasted dark spots, the performance of the
automatic and the semi-automatic methods is equivalent. However,
the fully automatic method did not provide acceptable geometries
in all cases. For these cases, the intelligent hybrid system was
validated, integrating the semi-automatic approach, using compact
and simple decision rules to request human intervention when
needed. This approach allows for the combining of benefits from
each approach, ensuring the quality of the classification when
fully automatic procedures are not satisfactory.",
doi = "10.1016/j.eswa.2017.03.037",
url = "http://dx.doi.org/10.1016/j.eswa.2017.03.037",
issn = "0957-4174",
language = "en",
targetfile = "genovez_intelligent.pdf",
urlaccessdate = "20 abr. 2024"
}