CONVOLUTIONAL NEURAL NETWORK BASED SEGMENTATION OF OIL SPREAD IN OCEAN

e-ISSN: 2582-5208 International Research Journal of Modernization in Engineering Technology and Science Volume:02/Issue:11/November -2020

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CONVOLUTIONAL NEURAL NETWORK BASED SEGMENTATION OF OIL SPREAD IN OCEAN R. Lakshmi *1, Swathi. S*2, G. Varsha*3, M.R.S. Thejjaswini*4 *1Assistant

professor, Department of Electronics and Instrumentation, Easwari Engineering College, Chennai, Tamilnadu, India.

*2,3,4

Student, Department of Electronics and Instrumentation, Easwari Engineering College, Chennai, Tamilnadu, India.

ABSTRACT A combination of airborne and satellite-based remote sensing is currently used for operational oil-spill monitoring worldwide. The image processing techniques such as unwanted noise reduction, followed by feature extraction at low level to identify lines, regions, occurrences and its areas with certain textures are used to start the identification of object in an image. The idea is to have these shapes as a combination of single objects like trucks on a road, weights on a conveyor belt or cellular cells on a microscope slide. Space borne satellite-based synthetic aperture radar SAR images shows an overview of large ocean areas, and surveillance aircraft can be directed to check possible oil-spill locations and also to verify the spill and finally to catch the polluter. Oil-spill detection is most effectively performed on a large scale using SAR images due to its allweather capabilities (given wind speeds in the range 2–14 m/s) and good coverage. Synthetic Aperture Radar SAR satellites are operated as it detects the possibility of location of oil spills. In this paper by using a neural network the oil spill region has been extracted in the radar image. The number of input images inside the datasets and the image recognition results derived from the segmentation procedures , provide important preconditions for oil spill accidents and also to identify oil spills in SAR images. Convolution neural network classifier is used to provide a fully automated oil spill identification system with the help of region segmentation.The convolution neural network comprises of three layers .The first is input layer followed by hidden layer and lastly output layer. The purpose of connecting the input layer with the hidden layer, and the hidden layer with the output layer of the images used is done with the help of a weight value matrix called grey level co-occurrence matrix is used.

Keywords—Marine oil spill, Convolution neural network, Synthetic Aperture Radar, BPN, Glcm.

I.

INTRODUCTION

Oil spills posses dangerous threats and great impact for the oceanic and coastal environments. Rather than humans physical measures , it could be better to use an automatic monitoring, detecting and controlling systems in order to reduce the spills caused due to oil in the oceans. It is also easier to conduct the operations at relevant times. The toxic chemicals contained in petroleum products like benzene, poly aromatic hydrocarbons and toluene are at large amounts in the oceans ,which were mainly due to human activity. This toxic chemicals results in the large destruction of marine ecosystem.The larvae, fish eggs and many sea creatures are greatly affected by the toxins present in the oil spills.These spills damage the birds feather as well as the fur of the animals.Thus they make them vulnerable to temperature fluctuations and these in turn results in impaired reproductive abilities. Clean up and recovery from an oil spill is a tedious process because when oil is covered with water, the ecosystem is not able to get any sunlight and it is deprived to oxygen. The recovery from spill might take weeks, months or even years.In order to rectify the above issues we have tried to implement a system which can identify oil spills in SAR images of dataset and the recognition results provide important preconditions for oil spill accident decision support. Convolution neural network classifier adopted with certain feature extraction procedures is used here to provide a fully automated oil spill and its segmentation is used to find the accuracy of the amount of oil spilled.

The back propagation network (BPN) and some previously used methods had some drawbacks like high computational load and poor discriminatory power. The local texture region is not accounted and this is the greatest disadvantage here. Accuracy terms have been identified at greater amounts in convolution network compared all other schemes. Thus we have added new Hybrid features which involves Grey level co-occurrence matrices and texture descriptors. There is also a clear understanding between BPN and CNN in terms of the www.irjmets.com

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e-ISSN: 2582-5208 International Research Journal of Modernization in Engineering Technology and Science Volume:02/Issue:11/November -2020

Impact Factor- 5.354

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features of the corresponding image. By including all these techniques we can achieve better accuracy as CNN is fast and better compatible in classification with its segmentation and low computational complexity.

II.

METHODOLOGY

The system developed in this paper makes use of SAR images in order to detect the oil spills in the ocean area and this satellite based synthetic aperture radar is capable of identyfing over any environmental conditions and operational time. Such approaches include the use of Convolutional neural networks(CNN). The flowchart is illustrated in Fig. 1. The new framework combining Colour Space Conversion,GLCM Features Extraction,NN Training and Classification, Fuzzy c-means clustering.

Fig.1 :Block diagram of proposed method 2.l. Major component of the system The major components of oil spill monitoring and simulation combined system are stated below.

SEGMENTATION:According to computer the image segmentation is a tedious process. Initially the input image is divided into smallest parts and each part has a value which can be from 0-255.The smallest point of the image is called as pixel. Segments refers to the parts of the image and the image is formed by combining all these parts.segmentation will transform pixel into larger components which is used to eliminate the necessity to observe individual pixel GLCM: Input image comprises of pixels, each with an intensity at specific grey level.GLCM is used to determine the number of times the grey level occurs in an image at different sections with the different level of intensity.In GLCM texture feature calculation can be used to determine the variation of intensity.

NN CLASIFICATON: Neural networks operates similar to the human brain where it develops classification rules and it is a complex model.Human brain makes use of neurons which Passes the information to the succeeding ones by obtaining the information from the preceeding one.similarly NN has multiple hidden layers and the data flow takes place from input to output layer through the hidden layer where the complex computation occurs.

Convolution neural network: It is the main feature used in image processing, image recognition, object detection and segmentation. For image recognition and detection used in this paper mainly needs pixel value and reaaranged in the form of partices. Input images are taken as pixels of array in order to compute thr image resolution.Convolution is the mathematical operation which takes the input image through multiple layers.The image that goes through multiple layers are pooling layer, normalization layer, fully connected layer and multiple convolution layer. When the input image in the datasets are large then those parameters can be www.irjmets.com @International Research Journal of Modernization in Engineering, Technology and Science

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reduced with the help of pooling layer .The matrix is transformed into vector and given to the fully connected layer.The features are combined in this layer and then normalization process is carried out thus classifying the images. The minimum pre-processing requires Shift invariant or Space invariant Artificial Neural Network (SIANN).SIANN depends on shared weights architecture.

Fig.2 convolutional neural network The CNN layers are categorized into 3 dimensions as weight, height and depth. The replicated filter units share the same parameters in which the CNN respond to same features regardless of their positions.Thus Convolution neural network have more efficiency because of its forward function and reduces the amount of parameter.

FUZZY C MEANS: Fuzzy Clustering means segmentation is used for the division of matrix and also clustering the set of data.The FCM algorithm is depending on the distance between the center of the image cluster and each pixel.The centres of the clusters are weighted by their degree. FCM reduces the overlapping of datasets, input feature map.

III.

MODELING AND ANALYSIS

Fig3. CNN-algorithm flow chart and parameter estimation BPN- FEATURE EXTRACTIONS: Energy : It is used to measure the homogeneity of the image and it could be obtained from the normalized COM. It is suitable for the measurement of detection of the disorders in texture image. www.irjmets.com

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Entropy : It gives the intensity levels which are measured as pixels from the image and it also gives better comparison results.

Where, p(i , j) is called the co-occurrence matrix

Fig.4. BPN result analysis Contrast : It shows the difference in luminance that is grey color level and thereby measures the depth and the color variation.

Correlation Coefficient : It gives the demographical or statistical relationship that occurs between the pixel pairs specified sum(sum((x- μx)(y-μy)p(x , y)/σxσy) Homogeneity: It describes the similar parts of the region between largest and smallest coverage. Here the similarity is shown between the GLCM elements to its diagonal. sum(sum(p(x , y)/(1 + [x-y]))) Where , (x,y) is the pixel colour (red,green,blue,grey level,black/white)

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IV.

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RESULTS AND DISCUSSION

Fig 5. Preprocessed image

Fig.6. Performance analysis

Fig.7 SAR image - oil spill in ocean

V.

CONCLUSION

This article helps to perform automatic segmentation using convolutional neural network in order to detect the oil spills in main areas and to avoid false positives.This technique helps to achieve the shape of the oil spill and also reduces the computational time.This technique is an improvement of the existing algorithms which are similar. High percentage of correctly classified pixels is relatively expected as a result of imbalance in the number of oil and background pixels. Even though clustering and logistic regression are considered useful for segmentation of oil spill, the combination of neural network and convolution technique will provide the best result.The data analysis and decision making are facilitated by integrating CNN algorithm with the decision support system and the end result is considered as the best performing segmentation. The future scope will

be associated with the low wind speed and this can greatly influence the oil spill appearance in the SAR image.

VI. [1]

[2] [3]

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