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

SAR image processing is an important way to detect marine oil spills. The detection efficiency and accuracy are the most important indicators. In order to detect oil spills more efficiently, a framework based on features and the extreme learning machine method is proposed in this paper. Texture feat...

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Bibliographic Details
Published in2018 3rd International Conference on Mechanical, Control and Computer Engineering (ICMCCE) pp. 559 - 563
Main Author Lyu, Xinrong
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2018
Subjects
Conferences
Correlation
Entropy
extreme learning machine
feature
gray level co-occurrence matrix
Handheld computers
Machine learning
oil spill
Tamura
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Summary:SAR image processing is an important way to detect marine oil spills. The detection efficiency and accuracy are the most important indicators. In order to detect oil spills more efficiently, a framework based on features and the extreme learning machine method is proposed in this paper. Texture feature is an effective approach for region of interest extraction in image processing, which is consistent with human visual sense. Both gray level co-occurrence matrix and Tamura features are selected to extract features from SAR images, which is different from general methods with denoising and image segmentation procedure. Then, a feature vector is constructed including all the features extracted, and the vector is taken as the input to train an extreme learning machine model. Through the training of many samples of SAR images, a final model for oil spill detection will be accomplished. A lot of detection experiments using this model show that the oil spill detection framework based on feature and extreme learning machine has higher accuracy and efficiency, and it can be carried out in practical application.
DOI:10.1109/ICMCCE.2018.00123