Deep-water oil-spill monitoring and recurrence analysis in the Brazilian territory using Sentinel-1 time series and deep learning

Author:

de Moura Nájla Vilar Aires,de Carvalho Osmar Luiz Ferreira,Gomes Roberto Arnaldo Trancoso,Guimarães Renato Fontes,de Carvalho Júnior Osmar Abílio

Funder

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Conselho Nacional de Desenvolvimento Cientifico e Tecnologico

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Computers in Earth Sciences,Earth-Surface Processes,Global and Planetary Change

Reference54 articles.

1. Boletim da Produção de Petróleo e Gás Natural;Agência Nacional do Petróleo,2021

2. Sensors, features, and machine learning for oil spill detection and monitoring: A review;Al-Ruzouq;Remote Sens.,2020

3. Instance segmentation of center pivot irrigation systems using multi-temporal SENTINEL-1 SAR images;de Albuquerque;Remote Sens. Appl. Soc. Environ.,2021

4. Oil spill detection by imaging radars: Challenges and pitfalls;Alpers;Remote Sens. Environ.,2017

5. Monitoring oil spill hotspots, contamination probability modelling and assessment of coastal impacts in the Caspian Sea using SENTINEL-1, LANDSAT-8, RADARSAT, ENVISAT and ERS satellite sensors;Bayramov;J. Oper. Oceanogr.,2018

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