Abstract
With the rapid development of deep learning, the neural network becomes an efficient approach for eddy detection. However, previous work employs a traditional neural network with a focus on improving the detecting accuracy only using limited data under a single scenario. Meanwhile, the experience of detecting eddies from one experiment is not directly inherited from the detection model for other experiments. Therefore, a cross-domain submesoscale eddy detection neural network (CDEDNet) based on the high-frequency radar (HFR) data of the Nansan and Xuwen region is proposed in this paper. Firstly, a fundamental deep eddy detection architecture CDEDNet-0 is constructed with a fully convolutional network (FCN). Secondly, for solving the problem of insufficient labeled eddy data, an instance-based domain adaption method is adopted in CDEDNet-1 to increase training samples. Thirdly, for tackling the problem of unable to inherit previous detection experience, parameter-based transfer learning is incorporated in CDEDNet-2 for multi-scene eddy detection. The experiment results demonstrate CDEDNet-1 and CDEDNet-2 perform better than CDEDNet-0 in terms of accuracy. Meanwhile, eddy characteristics including eddy type, radius, occurring time, merger, and dynamic trajectory are analyzed for the Nansan and Xuwen regions.
Highlights
Considering that deep eddy detection had been implemented for the Taiwan Strait dataset [39] and the small number of eddy observations associated with the South China Sea, the Taiwan Strait dataset was chosen as the source data, and the South China Sea dataset was regarded as the target data
In order to explore the eddy situation in Nansan and Xuwen, a crossdomain eddy detection neural network is constructed based on high-frequency radar (HFR) field data
Three architectures are presented for handling different challenges in deep eddy detection
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Deep learning-based methods include object detection-like pipelines and pixel segmentation-like pipelines. The object detection network firstly identifies the candidate region possibly with the eddy, reduces the area of the candidate region, and generates a bounding box without an overlapped area, it uses the VG method for localizing the eddy center and computing the eddy boundary in the bounding box. We attempt to use parameter-based transfer learning by sharing a general feature layer for eddy detection in multiple regions. A fully convolutional network skeleton-based eddy detection network CDEDNet-0, which is developed using the HFR data from the South China Sea is presented. For dealing with the problem of insufficient labeled data, we present an instance-based domain adaption method for increasing training samples in CDEDNet-1. For dealing with the problem of inheriting previous detection experience, CDEDNet-2 which incorporates parameter-based transfer learning, is designed for multi-scene eddy detection.
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