Oil Spill Detection Based on Features and Extreme Learning Machine Method in SAR Images

Lyu, Xinrong

doi:10.1109/icmcce.2018.00123

Cited by 9 publications

(7 citation statements)

References 2 publications

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“…While accuracy shows the level of accuracy of the model in correctly identifying. To get the value of precision, recall, and accuracy can be calculated through equation ( 10), ( 11) and (12).…”

Section: Discussionmentioning

confidence: 99%

“…From the value of the confusion matrix obtained, then look for the value of precision, recall and accuracy using equations ( 10), (11) and (12). The test results are presented in Table 1.…”

Section: Figure 7 Confusion Matrix From Developed Modelmentioning

confidence: 99%

“…This method has a faster learning speed than conventional artificial neural network methods such as backpropagation [11]. The ELM algorithm fixes the problems in the previous artificial neural network algorithms, especially in learning speed [12]. Several previous studies regarding the application of the ELM algorithm in digital image processing showed good accuracy results.…”

Section: Introductionmentioning

confidence: 99%

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Identification of Resistor Types Using Extreme Learning Machine Algorithms and Morphological Operation

Nuraini

2022

ijics

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Electronic components are the basic elements to form a series of electronic devices that are usually used in everyday life. For someone who studies the field of electricity, knowledge of electrical components is an important thing. One of the components whose use is most often found in electronic circuits is a resistor. However, some people do not know about these types of resistors. Especially for someone or a student who will learn about electronic components. This study aims to develop an image processing system that can identify transistor type images using the Extreme Learning Machine (ELM) algorithm. This algorithm performs integrated learning through a special form of feedforward perceptron which has one hidden layer. In order for the ELM algorithm to work properly, information about the features contained in the object to be identified is needed. So, in this study the ELM algorithm is combined with morphological characteristics through parameters such as area, perimeter, eccentricity, major axis length, and minor axis length. Based on these parameters, features will be obtained which will be input in the identification process. At the evaluation stage, the precision value was 87%, recall was 84.47% and accuracy was 85.5%.

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Section: Discussionmentioning

confidence: 99%

“…From the value of the confusion matrix obtained, then look for the value of precision, recall and accuracy using equations ( 10), (11) and (12). The test results are presented in Table 1.…”

Section: Figure 7 Confusion Matrix From Developed Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Identification of Resistor Types Using Extreme Learning Machine Algorithms and Morphological Operation

Nuraini

2022

ijics

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“…This creates significant difficulty for shipborne oil spill monitoring. Many research methods for spaceborne and airborne radar can be applied in the oil spill detection of shipborne radar images, including manual single threshold segmentation [28], the adaptive threshold segmentation method [29], the double threshold segmentation method [30], the active contour model (ACM) [31], neural networks, and machine learning algorithms [32,33]. The oil spill monitoring technology of shipborne radar remains in its infancy, and some related research findings have been published.…”

Section: Introductionmentioning

confidence: 99%

Oil Spill Monitoring of Shipborne Radar Image Features Using SVM and Local Adaptive Threshold

Wang

Cui

et al. 2020

Algorithms

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In the case of marine accidents, monitoring marine oil spills can provide an important basis for identifying liabilities and assessing the damage. Shipborne radar can ensure large-scale, real-time monitoring, in all weather, with high-resolution. It therefore has the potential for broad applications in oil spill monitoring. Considering the original gray-scale image from the shipborne radar acquired in the case of the Dalian 7.16 oil spill accident, a complete oil spill detection method is proposed. Firstly, the co-frequency interferences and speckles in the original image are eliminated by preprocessing. Secondly, the wave information is classified using a support vector machine (SVM), and the effective wave monitoring area is generated according to the gray distribution matrix. Finally, oil spills are detected by a local adaptive threshold and displayed on an electronic chart based on geographic information system (GIS). The results show that the SVM can extract the effective wave information from the original shipborne radar image, and the local adaptive threshold method has strong applicability for oil film segmentation. This method can provide a technical basis for real-time cleaning and liability determination in oil spill accidents.

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“…Machine learning of big data and multidimensional analyses techniques within PALM were used to analyze all relevant data from oil field, which let the company make the faster and better decision [22]. A new algorithm based on extreme learning machine model was proposed to determine the marine oil spill regions in SAR images [23]. A large number of historical data of oil and water wells were applied to monitor changes of some important parameters of the well site, which were used in the trend prediction and the early warning system [24].…”

Section: Introductionmentioning

confidence: 99%

An Sand Plug of Fracturing Intelligent Early Warning Model Embedded in Remote Monitoring System

et al. 2019

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In recent years, data mining technology has been widely used in different fields. For petroleum industry, the application of data mining method not only can analyze the data faster and more accurately but also can reduce the defects such as misjudgment and missed judgment caused by relying on artificial monitoring. Fracturing is one of the key techniques for increasing petroleum production. Due to the contingency of the abnormal variation of the fracturing construction curve, there is also a large lag and contingency by using the construction curve for sand block warning. Frequent false alarms and delayed alarms often occur. Therefore, the paper proposes an early-warning method of the sand plug of fracturing based on data mining. First, an early warning model of double logarithmic curve sand plug of fracturing is established, and the time series analysis algorithm is used to predict the oil pressure and casing pressure in the double logarithmic curve sand plug of fracturing risk warning model, so the early warning accuracy is improved. And then the general regression neural network (GRNN) algorithm is designed to optimize the prediction results of the time domain analysis. The improved affinity propagation (AP) clustering algorithm is used to cluster the monitoring data to help to improve the accuracy of the subsequent slope calculation, so as to improve the coincidence rate of the fracturing and sand plug risk. Finally, the risk warning model is applied and analyzed on site, and the validity and accuracy of the model are verified. The model is embedded in the remote monitoring system to realize online remote intelligent monitoring of risks by urban office workers. INDEX TERMS AP clustering, data mining, early warning, sand plug of fracturing, GRNN algorithm.

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Oil Spill Detection Based on Features and Extreme Learning Machine Method in SAR Images

Cited by 9 publications

References 2 publications

Identification of Resistor Types Using Extreme Learning Machine Algorithms and Morphological Operation

Identification of Resistor Types Using Extreme Learning Machine Algorithms and Morphological Operation

Oil Spill Monitoring of Shipborne Radar Image Features Using SVM and Local Adaptive Threshold

An Sand Plug of Fracturing Intelligent Early Warning Model Embedded in Remote Monitoring System

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