Brazilian Marine Animal Forests: A New World to Discover in the Southwestern Atlantic

Soares, Marcelo de Oliveira; Lotufo, Tito Monteiro da Cruz; Vieira, Leandro M.; Salani, Sula; Hajdu, Eduardo; Matthews-Cascon, Helena; Leão, Zelinda M.A.N.; Kikuchi, Ruy Kenji Papa de

doi:10.1007/978-3-319-17001-5_51-1

Cited by 3 publications

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“…The seas and oceans are natural resources of oil, natural gas, fauna, and flora, rich in biodiversity and ecosystems. Because of those attributes, the Brazilian marine area is known as the "Blue Amazon" [1]. The Campos Basin, one of the largest oil and gas producers in Brazil, is located on the northern coast of Rio de Janeiro, between the cities of Vitória (ES) and Cabo Frio (RJ), covering an area of 120,000 km 2 [2].…”

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confidence: 99%

Assessment of Machine Learning Techniques for Oil Rig Classification in C-Band SAR Images

et al. 2022

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This article aims at performing maritime target classification in SAR images using machine learning (ML) and deep learning (DL) techniques. In particular, the targets of interest are oil platforms and ships located in the Campos Basin, Brazil. Two convolutional neural networks (CNNs), VGG-16 and VGG-19, were used for attribute extraction. The logistic regression (LR), random forest (RF), support vector machine (SVM), k-nearest neighbours (kNN), decision tree (DT), naive Bayes (NB), neural networks (NET), and AdaBoost (ADBST) schemes were considered for classification. The target classification methods were evaluated using polarimetric images obtained from the C-band synthetic aperture radar (SAR) system Sentinel-1. Classifiers are assessed by the accuracy indicator. The LR, SVM, NET, and stacking results indicate better performance, with accuracy ranging from 84.1% to 85.5%. The Kruskal–Wallis test shows a significant difference with the tested classifier, indicating that some classifiers present different accuracy results. The optimizations provide results with more significant accuracy gains, making them competitive with those shown in the literature. There is no exact combination of methods for SAR image classification that will always guarantee the best accuracy. The optimizations performed in this article were for the specific data set of the Campos Basin, and results may change depending on the data set format and the number of images.

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Section: Introductionmentioning

confidence: 99%

Assessment of Machine Learning Techniques for Oil Rig Classification in C-Band SAR Images

et al. 2022

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Conservation status of the southernmost reef of the Amazon Reef System: the Parcel de Manuel Luís

et al. 2020

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Interconnected marine habitats form a single continental-scale reef system in South America

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Jucá-Queiroz

et al. 2022

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Large gaps in reef distribution may hinder the dispersal of marine organisms, interrupting processes vital to the maintenance of biodiversity. Here we show the presence and location of extensive reef habitats on the continental shelf between the Amazon Reef System (ARS) and the Eastern Brazilian Reef System (ERS), two reef complexes off eastern South America. Formations located 20–50 m deep include both biogenic and geogenic structures. The presence of diverse reef assemblages suggests the widespread occurrence of rocky substrates below 50 m. These habitats represent an expansion of both the ARS and ERS and the closure of the only remaining large-scale gap (~ 1000 km) among West Atlantic reef environments. This indicates that the SW Atlantic harbors a single, yet heterogeneous, reef system that stretches for about 4000 km, and thus, represents one of the largest semi-continuous tropical marine ecosystems in the world.

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Brazilian Marine Animal Forests: A New World to Discover in the Southwestern Atlantic

Cited by 3 publications

References 77 publications

Assessment of Machine Learning Techniques for Oil Rig Classification in C-Band SAR Images

Assessment of Machine Learning Techniques for Oil Rig Classification in C-Band SAR Images

Conservation status of the southernmost reef of the Amazon Reef System: the Parcel de Manuel Luís

Interconnected marine habitats form a single continental-scale reef system in South America

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