SAR Oil Spill Detection System through Random Forest Classifiers

Marcos Reinan Assis Conceição,Milton José Porsani,Carlos Alessandre Domingos Lentini,José Marques Lopes,Luis Felipe Ferreira de Mendonça,Mainara Biazati Gouveia,Rodrigo Nogueira de Vasconcelos,André Telles da Cunha Lima

doi:10.3390/rs13112044

Marcos Reinan Assis Conceição, Milton José Porsani + Show 6 more

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https://doi.org/10.3390/rs13112044

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Abstract

A set of open-source routines capable of identifying possible oil-like spills based on two random forest classifiers were developed and tested with a Sentinel-1 SAR image dataset. The first random forest model is an ocean SAR image classifier where the labeling inputs were oil spills, biological films, rain cells, low wind regions, clean sea surface, ships, and terrain. The second one was a SAR image oil detector named “Radar Image Oil Spill Seeker (RIOSS)”, which classified oil-like targets. An optimized feature space to serve as input to such classification models, both in terms of variance and computational efficiency, was developed. It involved an extensive search from 42 image attribute definitions based on their correlations and classifier-based importance estimative. This number included statistics, shape, fractal geometry, texture, and gradient-based attributes. Mixed adaptive thresholding was performed to calculate some of the features studied, returning consistent dark spot segmentation results. The selected attributes were also related to the imaged phenomena’s physical aspects. This process helped us apply the attributes to a random forest, increasing our algorithm’s accuracy up to 90% and its ability to generate even more reliable results.

Highlights

IntroductionMost coastal marine ecosystems have complex structural and dynamic characteristics, which can be quickly impacted by human activities [1]
The water contamination by oil and its derivatives is a matter of worldwide concern.Most coastal marine ecosystems have complex structural and dynamic characteristics, which can be quickly impacted by human activities [1]
Based on the three-step methodology described by [38,39] and in the research carried out by [18], we develop a new open-source methodology for detecting oil spills, written in Python language and open access on GitHub

Summary

Introduction

Most coastal marine ecosystems have complex structural and dynamic characteristics, which can be quickly impacted by human activities [1] Among these impacts, the oil exploration industry is responsible for a large part of hydrocarbons’ insertion in coastal environments. HPAs are hydrophobic chemical compounds that limit oil solubility in seawater, furthering the association with solid particles [3]. These low molecular weight compounds have a high toxicity. Knowing their sources, behavior, and distribution in the environment helps control human activities with the potential for environmental contamination. Ratio between foreground and background standard deviations.

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