Oil Spill Identification based on Dual Attention UNet Model Using Synthetic Aperture Radar Images

• Published in: Journal of the Indian Society of Remote Sensing Vol. 51; no. 1; pp. 121 - 133
• Main Authors: Mahmoud, Amira S., Mohamed, Sayed A., El-Khoriby, Reda A., AbdelSalam, Hisham M., El-Khodary, Ihab A.
• Format: Journal Article
• Language: English
• Published: New Delhi Springer India 01.01.2023; Springer Nature B.V
• Subjects: Coastal environments; Cognitive tasks; data collection; Deep learning; Earth and Environmental Science; Earth Sciences; Image segmentation; Machine learning; Machine translation; Marine ecosystems; Object recognition; Oil spills; oils; Radar; Radar imaging; Remote Sensing/Photogrammetry; Research Article; speech; Speech recognition; surface water; Synthetic aperture radar; Water bodies; Oil spill; Deep learning; Channel attention map; Synthetic Aperture Radar; Position attention map; UNet segmentation; Dual attention model
• ISSN: 0255-660X; 0974-3006
• DOI: 10.1007/s12524-022-01624-6

Oil spills cause tremendous damage to marine, coastal environments, and ecosystems. Previous deep learning-based studies have addressed the task of detecting oil spills as a semantic segmentation problem. However, further improvement is still required to address the noisy nature of the Synthetic Aperture Radar (SAR) imagery problem, which limits segmentation performance. In this study, a new deep learning model based on the Dual Attention Model (DAM) is developed to automatically detect oil spills in a water body. We enhanced a conventional UNet segmentation network by integrating a dual attention model DAM to selectively highlight the relevant and discriminative global and local characteristics of oil spills in SAR imagery. DAM is composed of a Channel Attention Map and a Position Attention Map which are stacked in the decoder network of UNet. The proposed DAM-UNet is compared with four baselines, namely fully convolutional network, PSPNet, LinkNet, and traditional UNet. The proposed DAM-UNet outperforms the four baselines, as demonstrated empirically. Moreover, the EG-Oil Spill dataset includes a large set of SAR images with 3000 image pairs. The obtained overall accuracy of the proposed method increased by 3.2% and reaches 94.2% compared with that of the traditional UNet. The study opens new development ideas for integrating attention modules into other deep learning tasks, including machine translation, image-based analysis, action recognition, and speech recognition.