The Application of Satellite Image Analysis in Oil Spill Detection

Tysiąc, Paweł; Strelets, Tatiana; Tuszyńska, Weronika

doi:10.3390/app12084016

Cited by 20 publications

(6 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Given the similarity between the Oil Spill dataset and the CSIRO dataset, we adhered to the color labeling standards employed in various studies for SAR images [6,24,31], as depicted in Figure 8. Although there are emerging datasets [32,33], one of the objectives of this research is to establish a standardized color representation.…”

Section: Classificationmentioning

confidence: 99%

OptimalNN: A Neural Network Architecture to Monitor Chemical Contamination in Cancer Alley

Udeji,

Margala

2024

JLPEA

View full text Add to dashboard Cite

The detrimental impact of toxic chemicals, gas, and oil spills in aquatic environments poses a severe threat to plants, animals, and human life. Regions such as Cancer Alley exemplify the profound consequences of inadequately controlled chemical spills, significantly affecting the local community. Given the far-reaching effects of these spills, it has become imperative to devise an efficient method for early monitoring, estimation, and cleanup, utilizing affordable and effective techniques. In this research, we explore the application of U-shaped neural Network (UNET) and U-shaped neural network transformer (UNETR) neural network models designed for the image segmentation of chemical and oil spills. Our models undergo training using the Commonwealth Scientific and Industrial Research Organization (CSIRO) dataset and the Oil Spill Detection dataset, employing a specialized filtering technique to enhance detection accuracy. We achieved training accuracies of 95.35% and 91% by applying UNET on the Oil Spill and the CSIRO datasets after 50 epochs of training, respectively. We also achieved a training accuracy of 75% by applying UNETR to the Oil Spill dataset. Additionally, we integrated mixed precision to expedite the model training process, thus maximizing data throughput. To further accelerate our implementation, we propose the utilization of the Field Programmable Gate Array (FPGA) architecture. The results obtained from our study demonstrate improvements in inference latency on FPGA.

show abstract

Section: Classificationmentioning

confidence: 99%

OptimalNN: A Neural Network Architecture to Monitor Chemical Contamination in Cancer Alley

Udeji,

Margala

2024

JLPEA

View full text Add to dashboard Cite

show abstract

“…That is, it can be more difficult to detect leaks if the soil has an initially high percentage of moisture [4]. Previous studies conducting water leak and oil spill detection also observed it to be a suitable technology [4,34]. The use of GIS can provide the general location of the leak, with infrared technology being used for accurate leak location.…”

Section: Remote Sensing and Ir Cameramentioning

confidence: 99%

An Integrated Approach to Leak Detection in Water Distribution Networks (WDNs) Using GIS and Remote Sensing

Al Hassani,

Ali,

Mortula

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

Leakages in the water distribution networks (WDNs) are real problems for utilities and other governmental agencies. Timely leak detection and location identification have been challenges. In this paper, an integrated approach to geospatial and infrared image processing was used for robust leak detection. The method combines drops in flow, pressure, and chlorine residuals to determine potential water leakage locations in the WDN using Geographic Information System (GIS) techniques. GIS layers were created from the hourly values of these three parameters for the city of Sharjah provided by the Sharjah Electricity, Water, and Gas Authority (SEWA). These layers are then analyzed for locations with dropped values of each of the parameters and are overlaid with each other. In the case where there were no overlaying locations between flow and pressure, further water quality analysis was avoided, assuming no potential leak. In the case where there are locations with drops in flow and pressure layers, these overlaying locations are then examined for drops in chlorine values. If overlaying locations are found, then these regions are considered potential leak locations. Once potential leak locations are identified, a specialized remote sensing technique can be used to pinpoint the leak location. This study also demonstrated the suitability of using an infrared camera for leak detection in a laboratory-based setup. This paper concludes that the following methodology can help water utility companies in the timely detection of leaks, saving money, time, and effort.

show abstract

“…Most current approaches for detecting oil pollution on the water surface involve either oil spill detection on the sea surface using various remote sensing monitoring methods to detect the type and area of the oil spill [10][11][12][13], or using remote sensing images to detect the oil slick [14,15]. Zhao Dong et al [16] proposed an oil slick detection method based on multispectral remote sensing technology for sea surface oil slick detection, but the method is less effective in detecting oil slicks heavily polluted by sunlight and needs to be verified by more multispectral images.…”

Section: Introductionmentioning

confidence: 99%

Global Relation-Aware-Based Oil Detection Method for Water Surface of Catchment Wells in Hydropower Stations

et al. 2023

View full text Add to dashboard Cite

The oil in hydropower station catchment wells is a source of water pollution which can cause the downstream river to become polluted. Timely detection of oil can effectively prevent the expansion of oil leakage and has important significance for protecting water sources. However, the poor environment and insufficient light on the water surface of catchment wells make oil pollution detection difficult, and the real-time performance is poor. To address these problems, this paper proposes a catchment well oil detection method based on the global relation-aware attention mechanism. By embedding the global relation-aware attention mechanism in the backbone network of Yolov5s, the main features of oil are highlighted and the minor information is suppressed at the spatial and channel levels, improving the detection accuracy. Additionally, to address the problem of partial loss of detail information in the dataset caused by the harsh environment of the catchment wells, such as dim light and limited area, single-scale retinex histogram equalization is used to improve the grayscale and contrast of the oil images, enhancing the details of the dataset images and suppressing the noise. The experimental results show that the accuracy of the proposed method achieves 94.1% and 89% in detecting engine oil and turbine oil pollution, respectively. Compared with the Yolov5s, Faster R-CNN, SSD, and FSSD detection algorithms, our method effectively reduces the problems of missing and false detection, and has certain reference significance for the detection of oil pollution on the water surface of catchment wells.

show abstract

The Application of Satellite Image Analysis in Oil Spill Detection

Cited by 20 publications

References 42 publications

OptimalNN: A Neural Network Architecture to Monitor Chemical Contamination in Cancer Alley

OptimalNN: A Neural Network Architecture to Monitor Chemical Contamination in Cancer Alley

An Integrated Approach to Leak Detection in Water Distribution Networks (WDNs) Using GIS and Remote Sensing

Global Relation-Aware-Based Oil Detection Method for Water Surface of Catchment Wells in Hydropower Stations

Contact Info

Product

Resources

About