Ocean Eddy Identification and Tracking Using Neural Networks

Franz, Katharina; Roscher, Ribana; Milioto, Andres; Wenzel, Susanne; Kusche, Jürgen

doi:10.1109/igarss.2018.8519261

Cited by 55 publications

(27 citation statements)

References 11 publications

(12 reference statements)

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“…Machine learning methods have also been used in previous studies to tackle altimetric eddy detection and tracking on the SSH field. In [32] and [33] a pixel-wise segmentation approach is adopted, with the original labeling of the train set stemming from geometrical eddy detection methods on the sea surface height field. Similarly in [34] the a geometrical eddy detection method is used to label training data derived from the velocity field.…”

Section: Why Deep Learning For Eddy Signature Classification?mentioning

confidence: 99%

Classification of Eddy Sea Surface Temperature Signatures Under Cloud Coverage

Moschos

Stegner

Schwander³

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Mesoscale oceanic eddies have a visible signature on Sea Surface Temperature (SST) satellite images, portraying diverse patterns of coherent vortices, temperature gradients and swirling filaments. However, learning the regularities of such signatures defines a challenging pattern recognition task, due to their complex structure but also to the cloud coverage which can corrupt a large fraction of the image. We introduce a novel Deep Learning approach to classify sea temperature eddy signatures, even if they are corrupted by strong cloud coverage. A large dataset of SST image patches is automatically retained and used to train a CNN-based classifier. Classification is performed with very high accuracy on coherent eddy signatures and is robust to a high level of cloud coverage, surpassing human expert efficiency on this task. This methodology can serve to validate and correct detections on satellite altimetry, the standard method used until now to track mesoscale eddies.

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Section: Why Deep Learning For Eddy Signature Classification?mentioning

confidence: 99%

Classification of Eddy Sea Surface Temperature Signatures Under Cloud Coverage

Moschos

Stegner

Schwander³

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…In the object detection branch, the common sequential structure is an objective detection neural network to locate the eddy’s position followed by the VG algorithm to determine the eddy’s center and boundary. Ocean Eddy Identification Neural Networks (OEDNet) was constructed for automatic identification and positioning of mesoscale eddies [ 27 ], the skeleton of which includes a RetinaNet, a deep residual network, and a feature pyramid network. It uses multiple SLA data to search for mesoscale eddies with small samples and in complex regions.…”

Section: Related Workmentioning

confidence: 99%

DEDNet: Offshore Eddy Detection and Location with HF Radar by Deep Learning

Liu

Zhou

Wen

2020

Sensors

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Oceanic eddy is a common natural phenomenon that has large influence on human activities, and the measurement and detection of offshore eddies are significant for oceanographic research. The previous classical detecting methods, such as the Okubo–Weiss algorithm (OW), vector geometry algorithm (VG), and winding angles algorithm (WA), not only depend on expert’s experiences to set an accurate threshold, but also need heavy calculations for large detection regions. Differently from the previous works, this paper proposes a deep eddy detection neural network with pixel segmentation skeleton on high frequency radar (HFR) data, namely, the deep eddy detection network (DEDNet). An offshore eddy detection dataset is firstly constructed, which has origins from the sea surface current data measured by two HFR systems on the South China Sea. Then, a spatial globally optimum and strong detail-distinguishing pixel segmentation network is presented to automatically detect and localize offshore eddies in a flow chart. An eddy detection network based on fully convolutional networks (FCN) is also presented for comparison with DEDNet. Experimental results show that DEDNet performs better than the FCN-based eddy detection network and is competitive with the classical statistics-based methods.

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“…Deep Learning has been rapidly gaining in popularity and solving problems in remote sensing [13], climate and the environment [14]. Machine learning methods have also been used in previous studies to tackle altimetric eddy detection and tracking on the SSH field via pixel-wise classification [15] or LSTM [16], as well as the velocity field [17]. Albeit their important contributions, they are restricted the limitations of the altimetry field perse (that is, its interpolation) on which the learning dataset is based.…”

Section: Introductionmentioning

confidence: 99%

Deep-SST-Eddies: A Deep Learning Framework to Detect Oceanic Eddies in Sea Surface Temperature Images

Moschos

Schwander²,

Stegner

et al. 2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Until now, mesoscale oceanic eddies have been automatically detected through physical methods on satellite altimetry. Nevertheless, they often have a visible signature on Sea Surface Temperature (SST) satellite images, which have not been yet sufficiently exploited. We introduce a novel method that employs Deep Learning to detect eddy signatures on such input. We provide the first available dataset for this task, retaining SST images through altimetric-based region proposal. We train a CNN-based classifier which succeeds in accurately detecting eddy signatures in well-defined examples. Our experiments show that the difficulty of classifying a large set of automatically retained images can be tackled by training on a smaller subset of manually labeled data. The difference in performance on the two sets is explained by the noisy automatic labeling and intrinsic complexity of the SST signal. This approach can provide to oceanographers a tool for validation of altimetric eddy detection through SST.

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Ocean Eddy Identification and Tracking Using Neural Networks

Cited by 55 publications

References 11 publications

Classification of Eddy Sea Surface Temperature Signatures Under Cloud Coverage

Classification of Eddy Sea Surface Temperature Signatures Under Cloud Coverage

DEDNet: Offshore Eddy Detection and Location with HF Radar by Deep Learning

Deep-SST-Eddies: A Deep Learning Framework to Detect Oceanic Eddies in Sea Surface Temperature Images

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