Oil-Spills Detection in Net-Sar Radar Images Using Support Vector Machine

Zhang, Dong; Guo, Liwei; Zeng, Jing; Xue-Bin, Zhou

doi:10.2174/1874444301507011958

Cited by 1 publication

(1 citation statement)

References 10 publications

(13 reference statements)

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“…Numerous machine learning techniques have been used for detecting oil spills. Decision trees [20], support vector machines (SVM) [21,22], random forests [23][24][25], and artificial neural networks (ANN) are the most common techniques described in the literature [26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Approaches for Oil Spill Detection: A Bibliometric Review of Research Trends and Challenges

Vasconcelos

Lima

Lentini

et al. 2023

JMSE

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Oil spill detection and mapping using deep learning (OSDMDL) is crucial for assessing its impact on coastal and marine ecosystems. A novel approach was employed in this study to evaluate the scientific literature in this field through bibliometric analysis and literature review. The Scopus database was used to evaluate the relevant scientific literature in this field, followed by a bibliometric analysis to extract additional information, such as architecture type, country collaboration, and most cited papers. The findings highlight significant advancements in oil detection at sea, with a strong correlation between technological evolution in detection methods and improved remote sensing data acquisition. Multilayer perceptrons (MLP) emerged as the most prominent neural network architecture in 11 studies, followed by a convolutional neural network (CNN) in 5 studies. U-Net, DeepLabv3+, and fully convolutional network (FCN) were each used in three studies, demonstrating their relative significance too. The analysis provides insights into collaboration, interdisciplinarity, and research methodology and contributes to the development of more effective policies, strategies, and technologies for mitigating the environmental impact of oil spills in OSDMDL.

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