2023
DOI: 10.3390/rs15092244
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Eddy Detection in the Marginal Ice Zone with Sentinel-1 Data Using YOLOv5

Abstract: The automatic detection and analysis of ocean eddies in the marginal ice zone via remote sensing is a very challenging task but of critical importance for scientific applications and anthropogenic activities. Therefore, as one of the first steps toward the automation of the eddy detection process, we investigated the potential of applying YOLOv5, a deep convolutional neural network architecture, to specifically collected and labeled high-resolution synthetic aperture radar data for a very dynamic area over the… Show more

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Cited by 6 publications
(2 citation statements)
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“…In recent years, an increasing number of studies have been conducted on the detection of eddies using SAR data [17,27,33,[44][45][46]]. Among them, several studies have utilized deep learning approaches to automatically detect eddies in SAR images.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…In recent years, an increasing number of studies have been conducted on the detection of eddies using SAR data [17,27,33,[44][45][46]]. Among them, several studies have utilized deep learning approaches to automatically detect eddies in SAR images.…”
Section: Introductionmentioning
confidence: 99%
“…It is noteworthy that the MFNN model does not provide identification for individual instances of eddies. Xia et al [17] and Khachatrian et al [45] employed YOLO-based networks [48] to detect the bounding boxes of eddies. However, compared to pixel-wise masks that provide detailed information for each object instance, the limited information provided by bounding boxes may not be sufficient for various downstream tasks, such as eddy parameter inversion.…”
Section: Introductionmentioning
confidence: 99%