EddyNet: A Deep Neural Network For Pixel-Wise Classification of Oceanic Eddies

Lguensat, Redouane; Sun, Miao; Fablet, Ronan; Tandeo, Pierre; Mason, Evan; Chen, Ge

doi:10.1109/igarss.2018.8518411

Cited by 114 publications

(77 citation statements)

References 30 publications

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“…Segmentation methods based on deep learning can be handled by supervised learning with adequate training data . To build a reliable segmentation model, a prerequisite is the availability of a large amount of labeled training data.…”

Section: Deep‐learning Methodsmentioning

confidence: 99%

Machine learning techniques for biomedical image segmentation: An overview of technical aspects and introduction to state‐of‐art applications

et al. 2020

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In recent years, significant progress has been made in developing more accurate and efficient machine learning algorithms for segmentation of medical and natural images. In this review article, we highlight the imperative role of machine learning algorithms in enabling efficient and accurate segmentation in the field of medical imaging. We specifically focus on several key studies pertaining to the application of machine learning methods to biomedical image segmentation. We review classical machine learning algorithms such as Markov random fields, k‐means clustering, random forest, etc. Although such classical learning models are often less accurate compared to the deep‐learning techniques, they are often more sample efficient and have a less complex structure. We also review different deep‐learning architectures, such as the artificial neural networks (ANNs), the convolutional neural networks (CNNs), and the recurrent neural networks (RNNs), and present the segmentation results attained by those learning models that were published in the past 3 yr. We highlight the successes and limitations of each machine learning paradigm. In addition, we discuss several challenges related to the training of different machine learning models, and we present some heuristics to address those challenges.

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Section: Deep‐learning Methodsmentioning

confidence: 99%

Machine learning techniques for biomedical image segmentation: An overview of technical aspects and introduction to state‐of‐art applications

et al. 2020

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“…In the field of object detection and ocean eddy extraction, four typical metrics are used to measure the performance of a method: precision, recall, F measure , and execution time [29,30]:…”

Section: Resultsmentioning

confidence: 99%

“…In view of the simple characteristics of oceanic mesoscale eddy samples and the accurate target location, the side connection of the feature pyramid network can be used to better identify small objects. As it is different from the network structure in [29,30], OEDNet can detect the task as a regression problem rather than a simple classification whose inputs are SLA contour maps and eddy annotation .xml files. By scanning the entire image at one time, through multiple layers of convolution processing, the network output is characteristic of the grid.…”

Section: Network Structurementioning

confidence: 99%

Oceanic Mesoscale Eddy Detection Method Based on Deep Learning

Duo

Wang

2019

Remote Sensing

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Oceanic mesoscale eddies greatly influence energy and matter transport and acoustic propagation. However, the traditional detection method for oceanic mesoscale eddies relies too much on the threshold value and has significant subjectivity. The existing machine learning methods are not mature or purposeful enough, as their train set lacks authority. In view of the above problems, this paper constructs a mesoscale eddy automatic identification and positioning network—OEDNet—based on an object detection network. Firstly, 2D image processing technology is used to enhance the data of a small number of accurate eddy samples annotated by marine experts to generate the train set. Then, the object detection model with a deep residual network, and a feature pyramid network as the main structure, is designed and optimized for small samples and complex regions in the mesoscale eddies of the ocean. Experimental results show that the model achieves better recognition compared to the traditional detection method and exhibits a good generalization ability in different sea areas.

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“…In [44], the authors proposed the deep residual learning framework that uses new connections to simplify training, instead of using skip connection in deep networks. Lguensat et al [29] proposed a model called EddyNet, a deep learning architecture for presetting eddy recognition and classification using the Sea Surface level maps supplied by the Copernicus Marine and Situation Monitoring Service. EddyNet contains a convolutional encoder-decoder layer for the pixel-wise classification.…”

Section: Related Workmentioning

confidence: 99%

A Novel Deep Structure U-Net for Sea-Land Segmentation in Remote Sensing Images

Shamsolmoali

Zareapoor

Wang

et al. 2019

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

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Sea-land segmentation is an important process for many key applications in remote sensing. Proper operative sealand segmentation for remote sensing images remains a challenging issue due to complex and diverse transition between sea and lands. Although several Convolutional Neural Networks (CNNs) have been developed for sea-land segmentation, the performance of these CNNs is far from the expected target. This paper presents a novel deep neural network structure for pixelwise sea-land segmentation, a Residual Dense U-Net (RDU-Net), in complex and high-density remote sensing images. RDU-Net is a combination of both downsampling and upsampling paths to achieve satisfactory results. In each down-and up-sampling path, in addition to the convolution layers, several densely connected residual network blocks are proposed to systematically aggregate multi-scale contextual information. Each dense network block contains multilevel convolution layers, short-range connections and an identity mapping connection which facilitates features reuse in the network and makes full use of the hierarchical features from the original images. These proposed blocks have a certain number of connections that are designed with shorter distance backpropagation between the layers and can significantly improve segmentation results whilst minimizing computational costs. We have performed extensive experiments on two real datasets Google-Earth and ISPRS and compare the proposed RDU-Net against several variations of Dense Networks. The experimental results show that RDU-Net outperforms the other state-of-the-art approaches on the sea-land segmentation tasks.Index Term-Deep neural network (DNN), dense network (DenseNet), remote sensing images, sea-land segmentation, U-Net.

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EddyNet: A Deep Neural Network For Pixel-Wise Classification of Oceanic Eddies

Cited by 114 publications

References 30 publications

Machine learning techniques for biomedical image segmentation: An overview of technical aspects and introduction to state‐of‐art applications

Machine learning techniques for biomedical image segmentation: An overview of technical aspects and introduction to state‐of‐art applications

Oceanic Mesoscale Eddy Detection Method Based on Deep Learning

A Novel Deep Structure U-Net for Sea-Land Segmentation in Remote Sensing Images

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