Semantic Segmentation of Mesoscale Eddies in the Arabian Sea: A Deep Learning Approach

Hammoud, Mohamad Abed El Rahman1; Zhan, Peng1,2,3; Hakla, Omar4; Knio, Omar1,2; Hoteit, Ibrahim1,2

2023-03-01

10.3390/rs15061525

REMOTE SENSING   Impact Factor & Quartile

2072-4292

Detecting mesoscale ocean eddies provides a better understanding of the oceanic processes that govern the transport of salt, heat, and carbon. Established eddy detection techniques rely on physical or geometric criteria, and they notoriously fail to predict eddies that are neither circular nor elliptical in shape. Recently, deep learning techniques have been applied for semantic segmentation of mesoscale eddies, relying on the outputs of traditional eddy detection algorithms to supervise the training of the neural network. However, this approach limits the network's predictions because the available annotations are either circular or elliptical. Moreover, current approaches depend on the sea-surface height, temperature, or currents as inputs to the network, and these data may not provide all the information necessary to accurately segment eddies. In the present work, we have trained a neural network for the semantic segmentation of eddies using human-based-and expert-validated-annotations of eddies in the Arabian Sea. Training with human-annotated datasets enables the network predictions to include more complex geometries, which occur commonly in the real ocean. We then examine the impact of different combinations of input surface variables on the segmentation performance of the network. The results indicate that providing additional surface variables as inputs to the network improves the accuracy of the predictions by approximately 5%. We have further fine-tuned another pre-trained neural network to segment eddies and achieved a reduced overall training time and higher accuracy compared to the results from a network trained from scratch.

eddy detection deep neural network semantic segmentation Arabian Sea

SCI

English

Corresponding

Virtual Red Sea Initiative Award[REP/1/3268-01-01]

Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology

Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology

WOS:000960593200001

MDPI

Web of Science

1.King Abdullah Univ Sci & Technol, Phys Sci & Engn, Thuwal 23955, Saudi Arabia
2.Southern Univ Sci & Technol, Dept Ocean Sci & Engn, Shenzhen 518055, Peoples R China
3.Southern Marine Sci & Engn Guangdong Lab, Guangzhou 510000, Peoples R China
4.American Univ Beirut, Maroun Semaan Fac Engn & Architecture, Beirut 1107, Lebanon

Hammoud, Mohamad Abed El Rahman,Zhan, Peng,Hakla, Omar,et al. Semantic Segmentation of Mesoscale Eddies in the Arabian Sea: A Deep Learning Approach[J]. REMOTE SENSING,2023,15(6).

Hammoud, Mohamad Abed El Rahman,Zhan, Peng,Hakla, Omar,Knio, Omar,&Hoteit, Ibrahim.(2023).Semantic Segmentation of Mesoscale Eddies in the Arabian Sea: A Deep Learning Approach.REMOTE SENSING,15(6).

Hammoud, Mohamad Abed El Rahman,et al."Semantic Segmentation of Mesoscale Eddies in the Arabian Sea: A Deep Learning Approach".REMOTE SENSING 15.6(2023).