Volume 19, Issue 3 (JIAEEE Vol.19 No.3 2022)
Journal of Iranian Association of Electrical and Electronics Engineers 2022, 19(3): 131-144 |
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Aghaei N, Akbarizadeh G, Kosarian A. Using ShuffleNet to design a deep semantic segmentation model for oil spill detection in synthetic aperture radar images. Journal of Iranian Association of Electrical and Electronics Engineers 2022; 19 (3) :131-144
URL: http://jiaeee.com/article-1-1397-en.html
URL: http://jiaeee.com/article-1-1397-en.html
Shahid Chamran University of Ahvaz
Abstract: (801 Views)
Deep Semantic segmentation of images as an integrated solution for image analysis is based on the classification of individual image pixels, especially in applications such as oil spill detection in the marine areas which have no clear boundaries. To curb pollution and environmental hazards caused by oil spills, it is important to provide more accurate algorithms. Synthetic aperture radar images are widely used in the oil spill detection field. In these images, there are challenges such as speckle noise as well as distinguishing between oil spills and lookalike areas. The application of new machine learning methods can help reduce the involvement of human taste in decision-making. In this paper, the feature channel shuffling method on CNN networks, atrous block, and decoder parts are used and the computational complexity is drastically reduced and also provides much better oil spill segmentation results than other methods. The proposed network architecture is based on the Vgg16 architecture. The overall accuracy, accuracy, intersection over :union:, weighted IoU, and BF score is used as evaluation parameters. In the proposed method, the accuracy of detecting the oil spills and look-alikes was improved by 7.8% and 7.3%, respectively, compared to the previous simulated methods.
Keywords: Channel shuffle, ShuffleNet, deep learning, semantic segmentation, oil spill detection, synthetic aperture radar images
Type of Article: Research |
Subject:
Electronic
Received: 2021/11/8 | Accepted: 2022/05/7 | Published: 2022/09/2
Received: 2021/11/8 | Accepted: 2022/05/7 | Published: 2022/09/2
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