Open Access
Subscription Access
Open Access
Subscription Access
A Deep Learning Based Analysis of Oil Spilled Images To Minimize Pollution in Marine Environment
Subscribe/Renew Journal
The rising demand for oil and increased shipping capacity are significant contributors to the pollution of the world seas and oceans that is caused by human activity. Oil spills on the world waterways are another major cause of this pollution. Because of the growing demand for oil and the capability of the maritime transport industry, oil spills on seas and oceans have become a significant source of pollution in recent years. It is of the utmost importance that oil spills are constantly monitored and that measures are taken to clean them up as quickly as is humanly possible. This is since oil spills can have devastating effects not only on the local ecosystem but also on the economies of states that are located along the shore. Because of the ongoing threats that are posed to marine life, biodiversity, and habitats, it is of the utmost importance to be able to keep a watch on oil spills from a distance, recognise them, and take action to clean them up. This is essential. In the past ten years, developments in remote sensing data collection, computing capability, cloud computing infrastructure, and cuttingedge SqueezeNet algorithms have led to significant advancements in oil spill detection. These developments have been responsible for most of the progress. These technological advancements have made it possible to identify oil spills more accurately.
Keywords
Oil Spill, Shipping, Pollution, SqueezeNet.
Subscription
Login to verify subscription
User
Font Size
Information
- M. Rajalakshmi and C. Karthik, “Machine Learning for Modeling and Control of Industrial Clarifier Process”, Intelligent Automation and Soft Computing, Vol. 32, No. 1, pp. 1-12, 2022.
- M. Shaban, R. Salim and A. El-Baz, “A Deep-Learning Framework for the Detection of Oil Spills from SAR Data”, Sensors, Vol. 21, No. 7, pp. 2351-2358, 2021.
- J. Zhang and J. Li, “Oil Spill Detection in Quad-Polarimetric SAR Images using an Advanced Convolutional Neural Network based on SuperPixel Model”, Remote Sensing, Vol. 12, No. 6, pp. 944-956, 2020.
- S. Silvia Priscila and M. Ramkumar, “Interactive Artificial Neural Network Model for UX Design”, Proceedings of International Conference on Computing, Communication, Electrical and Biomedical Systems, pp. 277-284, 2022.
- G. Li and L. Wang, Marine Oil Slick Detection using Improved Polarimetric Feature Parameters based on Polarimetric Synthetic Aperture Radar Data”, Remote Sensing, Vol. 13, No. 9, pp. 1607-1613, 2021.
- A.S. Dhavalikar, and P.C. Choudhari, “Detection and Quantification of Daily Marine Oil Pollution using Remote Sensing”, Water, Air, and Soil Pollution, Vol. 233, No. 8, pp. 336-345, 2022.
- N. Aghaei and A. Kosarian, “Using ShuffleNet to Design a Deep Semantic Segmentation Model for Oil Spill Detection in Synthetic Aperture Radar Images”, Journal of Iranian Association of Electrical and Electronics Engineers, Vol. 19, No. 3, pp. 131-144, 2022.
- N.V.A. De Moura and O.A. De Carvalho Junior, “DeepWater Oil-Spill Monitoring and Recurrence Analysis in the Brazilian Territory using Sentinel-1 Time Series and Deep Learning”, International Journal of Applied Earth Observation and Geoinformation, Vol. 107, pp. 102695- 102699, 2022.
- M. Krestenitis, “Oil Spill Identification from Satellite Images using Deep Neural Networks”, Remote Sensing, Vol. 11, No. 15, pp. 1762-1776, 2019.
- M. Krestenitis, “Early Identification of Oil Spills in Satellite Images using deep CNNs”, Proceedings of International Conference on MultiMedia Modeling, pp. 424-435, 2019.
Abstract Views: 203
PDF Views: 0