W-Net: A Deep Network for Simultaneous Identification of Gulf Stream and Rings from Concurrent Satellite Images of Sea Surface Temperature and Height

Lambhate, D and Sharma, R and Clark, J and Gangopadhyay, A and Subramani, D (2021) W-Net: A Deep Network for Simultaneous Identification of Gulf Stream and Rings from Concurrent Satellite Images of Sea Surface Temperature and Height. In: IEEE Transactions on Geoscience and Remote Sensing, 60 .

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Abstract

Accurate digitization of synoptic ocean features is crucial for climate studies and the operational forecasting of ocean and coupled ocean-atmosphere systems. Today, for some North Atlantic operational regional models, skilled human experts visualize and extract the gulf stream and rings (warm and cold eddies) through an extensive and knowledge-based manual process. To automate this task, we develop a dynamics-inspired deep learning system that extracts the Gulf Stream and rings from concurrent satellite images of sea surface temperature (SST) and sea surface height (SSH). We pose the above problem as a multilabel semantic image segmentation problem. A novel deep convolutional neural network architecture named W-Net, with two parallel encoder-decoder branches, is developed to perform the segmentation. The W-Net's one branch is the SST branch (accepts SST image as input) and another is the SSH branch (accepts SSH as input), and the final output is a segmentation of gulf stream, warm rings, and cold rings. A dataset consisting of SST, SSH, and manual feature annotation (ground truth) from 2014 to 2018 is used for training. For gulf stream, we obtain 82.7 raw test accuracy and a low error of 4.39 in the detected path length. For the Rings, we obtain more than 71 raw eddy detection accuracy. A detailed ablation study and an examination of both SST and SSH parts of the network are presented to understand how the deep neural network learns to segment the gulf stream's meandering path and Rings accurately.

Item Type:	Journal Article
Publication:	IEEE Transactions on Geoscience and Remote Sensing
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	The copyright for this article belongs to the Institute of Electrical and Electronics Engineers Inc.
Keywords:	Atmospheric temperature; Convolutional neural networks; Deep learning; Deep neural networks; Image segmentation; Knowledge based systems; Learning systems; Network architecture; Ocean currents; Semantics; Submarine geophysics; Surface properties; Surface waters, Feature annotation; Ocean-atmosphere system; Operational forecasting; Satellite images; Sea surface height; Sea surface temperature (SST); Semantic image segmentations; Simultaneous identification, Internet protocols, accuracy assessment; artificial neural network; atmosphere-ocean coupling; identification method; satellite imagery; sea surface height; sea surface temperature, Atlantic Ocean; Gulf Stream
Department/Centre:	Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited:	14 Jul 2022 05:13
Last Modified:	14 Jul 2022 05:13
URI:	https://eprints.iisc.ac.in/id/eprint/74362

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