Banca de DEFESA: ANA CLAUDIA SOUZA VIDAL DE NEGREIROS

Uma banca de DEFESA de DOUTORADO foi cadastrada pelo programa.
STUDENT : ANA CLAUDIA SOUZA VIDAL DE NEGREIROS
DATE: 15/07/2022
TIME: 08:30
LOCAL: Online
TITLE:

A NOVEL AUTOMATED OIL SPILL DETECTION APPROACH BASED ON THE q-
EXPONENTIAL DISTRIBUTION AND MACHINE LEARNING MODELS

KEY WORDS:

q-Exponential distribution, feature extraction, machine learning, computer vision,
oil spills, risk analysis.

PAGES: 117
BIG AREA: Engenharias
AREA: Engenharia de Produção
SUMMARY:

Oil spills are among the most undesirable events in coastal environments because they are
substantially harmful, with negative environmental, social, and economic consequences. In
general, a risk framework for the event involves prevention, monitoring, detection, and damage
mitigation. Regarding detection, rapid oil spill identification is essential for problem mitigation,
which generally fosters the use of automated procedures. Usually, automated oil spill detection
involves radar images, computer vision, and machine learning techniques to classify these
images. In this work, we propose a novel image feature extraction method based on the q-
Exponential probability distribution, named q-EFE. Such a probabilistic model is suitable to
account for atypical extreme values of the variable of interest, e.g., pixels values, as it can have
the power-law behavior. The q-EFE part is combined with machine learning methods to
comprise a computer vision methodology to automatically classify images as “with oil spill” or
“without oil spill”. Hence, we also propose a new automatic oil spill detection methodology
that uses the q-EFE to rapidly identify oil spills in radar images. We used a public dataset
composed of 1112 Synthetic Aperture Radar (SAR) images to validate our proposed
methodology. Considering the proposed q-Exponential-based feature extraction, the tested
Machine Learning methods and Deep Learning models architectures, the Support Vector
Machine (SVM) and Extreme Gradient Boosting (XGB) models outperformed deep learning
models and Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM)
techniques for the biggest dataset size.

BANKING MEMBERS:
Externa à Instituição - FÁTIMA NELSIZEUMA SOMBRA DE MEDEIROS
Externo à Instituição - LUIS FELIPE FERREIRA DE MENDONÇA
Interno - 1549303 - CRISTIANO ALEXANDRE VIRGINIO CAVALCANTE
Presidente - 2732514 - ISIS DIDIER LINS
Interno - 2766188 - MARCIO JOSE DAS CHAGAS MOURA