The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Le radar à ondes de surface à haute fréquence (HFSWR) peut réaliser une détection au-dessus de l'horizon, qui peut détecter et suivre efficacement les navires et les avions à très basse altitude, ainsi que l'acquisition d'informations sur l'état de la mer telles que les icebergs et les courants océaniques, etc. . Cependant, le HFSWR est sérieusement affecté par les parasites, notamment les parasites marins et les parasites ionosphériques. Dans cet article, nous proposons une méthode de segmentation sémantique d'images d'apprentissage profond basée sur le réseau Deeplabv3+ optimisé pour réaliser la détection automatique du fouillis marin et du fouillis ionosphérique en utilisant les images spectrales RD mesurées du HFSWR pendant le typhon comme données expérimentales, ce qui évite l'inconvénient des méthodes traditionnelles. des méthodes de détection qui nécessitent une grande quantité de connaissances a priori et fournissent une base pour la suppression ultérieure du fouillis ou la recherche sur les caractéristiques du fouillis.
Haotian CHEN
Hebei GEO University,Kunsan National University
Sukhoon LEE
Kunsan National University
Di YAO
Harbin Institute of Technology
Dongwon JEONG
Kunsan National University
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Haotian CHEN, Sukhoon LEE, Di YAO, Dongwon JEONG, "Sea Clutter Image Segmentation Method of High Frequency Surface Wave Radar Based on the Improved Deeplab Network" in IEICE TRANSACTIONS on Fundamentals,
vol. E105-A, no. 4, pp. 730-733, April 2022, doi: 10.1587/transfun.2021EAL2057.
Abstract: High Frequency Surface Wave Radar (HFSWR) can achieve over-the-horizon detection, which can effectively detect and track the ships and ultra-low altitude aircrafts, as well as the acquisition of sea state information such as icebergs and ocean currents and so on. However, HFSWR is seriously affected by the clutters, especially sea clutter and ionospheric clutter. In this paper, we propose a deep learning image semantic segmentation method based on optimized Deeplabv3+ network to achieve the automatic detection of sea clutter and ionospheric clutter using the measured R-D spectrum images of HFSWR during the typhoon as experimental data, which avoids the disadvantage of traditional detection methods that require a large amount of a priori knowledge and provides a basis for subsequent the clutter suppression or the clutter characteristics research.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2021EAL2057/_p
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@ARTICLE{e105-a_4_730,
author={Haotian CHEN, Sukhoon LEE, Di YAO, Dongwon JEONG, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Sea Clutter Image Segmentation Method of High Frequency Surface Wave Radar Based on the Improved Deeplab Network},
year={2022},
volume={E105-A},
number={4},
pages={730-733},
abstract={High Frequency Surface Wave Radar (HFSWR) can achieve over-the-horizon detection, which can effectively detect and track the ships and ultra-low altitude aircrafts, as well as the acquisition of sea state information such as icebergs and ocean currents and so on. However, HFSWR is seriously affected by the clutters, especially sea clutter and ionospheric clutter. In this paper, we propose a deep learning image semantic segmentation method based on optimized Deeplabv3+ network to achieve the automatic detection of sea clutter and ionospheric clutter using the measured R-D spectrum images of HFSWR during the typhoon as experimental data, which avoids the disadvantage of traditional detection methods that require a large amount of a priori knowledge and provides a basis for subsequent the clutter suppression or the clutter characteristics research.},
keywords={},
doi={10.1587/transfun.2021EAL2057},
ISSN={1745-1337},
month={April},}
Copier
TY - JOUR
TI - Sea Clutter Image Segmentation Method of High Frequency Surface Wave Radar Based on the Improved Deeplab Network
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 730
EP - 733
AU - Haotian CHEN
AU - Sukhoon LEE
AU - Di YAO
AU - Dongwon JEONG
PY - 2022
DO - 10.1587/transfun.2021EAL2057
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E105-A
IS - 4
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - April 2022
AB - High Frequency Surface Wave Radar (HFSWR) can achieve over-the-horizon detection, which can effectively detect and track the ships and ultra-low altitude aircrafts, as well as the acquisition of sea state information such as icebergs and ocean currents and so on. However, HFSWR is seriously affected by the clutters, especially sea clutter and ionospheric clutter. In this paper, we propose a deep learning image semantic segmentation method based on optimized Deeplabv3+ network to achieve the automatic detection of sea clutter and ionospheric clutter using the measured R-D spectrum images of HFSWR during the typhoon as experimental data, which avoids the disadvantage of traditional detection methods that require a large amount of a priori knowledge and provides a basis for subsequent the clutter suppression or the clutter characteristics research.
ER -