DEDNet: Offshore Eddy Detection and Location with HF Radar by Deep Learning
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),...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-12-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/21/1/126 |
id |
doaj-1bfe7b03d9ee41ca8a1b1a2459b1e358 |
---|---|
record_format |
Article |
spelling |
doaj-1bfe7b03d9ee41ca8a1b1a2459b1e3582020-12-29T00:00:22ZengMDPI AGSensors1424-82202021-12-012112612610.3390/s21010126DEDNet: Offshore Eddy Detection and Location with HF Radar by Deep LearningFangyuan Liu0Hao Zhou1Biyang Wen2School of Electronic Information, Wuhan University, Wuhan 430072, ChinaSchool of Electronic Information, Wuhan University, Wuhan 430072, ChinaSchool of Electronic Information, Wuhan University, Wuhan 430072, ChinaOceanic 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.https://www.mdpi.com/1424-8220/21/1/126eddy detectionpixel-wise segmentationhigh frequency radarsea current |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Fangyuan Liu Hao Zhou Biyang Wen |
spellingShingle |
Fangyuan Liu Hao Zhou Biyang Wen DEDNet: Offshore Eddy Detection and Location with HF Radar by Deep Learning Sensors eddy detection pixel-wise segmentation high frequency radar sea current |
author_facet |
Fangyuan Liu Hao Zhou Biyang Wen |
author_sort |
Fangyuan Liu |
title |
DEDNet: Offshore Eddy Detection and Location with HF Radar by Deep Learning |
title_short |
DEDNet: Offshore Eddy Detection and Location with HF Radar by Deep Learning |
title_full |
DEDNet: Offshore Eddy Detection and Location with HF Radar by Deep Learning |
title_fullStr |
DEDNet: Offshore Eddy Detection and Location with HF Radar by Deep Learning |
title_full_unstemmed |
DEDNet: Offshore Eddy Detection and Location with HF Radar by Deep Learning |
title_sort |
dednet: offshore eddy detection and location with hf radar by deep learning |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2021-12-01 |
description |
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. |
topic |
eddy detection pixel-wise segmentation high frequency radar sea current |
url |
https://www.mdpi.com/1424-8220/21/1/126 |
work_keys_str_mv |
AT fangyuanliu dednetoffshoreeddydetectionandlocationwithhfradarbydeeplearning AT haozhou dednetoffshoreeddydetectionandlocationwithhfradarbydeeplearning AT biyangwen dednetoffshoreeddydetectionandlocationwithhfradarbydeeplearning |
_version_ |
1724368240762683392 |