A Deep Learning Method for Offshore Raft Aquaculture Extraction Based on Medium-Resolution Remote Sensing Images

Liu, Jin; Lu, Yimin; Guo, Xiangzhong; Ke, Wenhui

doi:10.1109/jstars.2023.3291499

Cited by 5 publications

(4 citation statements)

References 51 publications

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“…The largest share of Deep Learning approaches was represented by the encoder-decoder architecture UNet. With 12 applications [31,39,50,62,[67][68][69]72,73,80,83,115], UNet is the most frequently used technology of all the articles reviewed. The U-Net model, originally developed for biomedical image segmentation [116], gained considerable attention due to its outstanding performance when published and its clear, structured design.…”

Section: Methods Usedmentioning

confidence: 99%

“…OWFs were investigated six times from 2020 on Concentrating on thematic foci, aquaculture was investigated in 57 publications and was therefore the most commonly studied infrastructure type in this review. Raft aquaculture was the most studied (46%) [31,32,37,39,46,50,[55][56][57][58][59][60][61][62][63][64][65][66][67][68][69][70][71][72][73] and 12% of aquaculture studies looked specifically at cages [40,52,[74][75][76][77]. The remaining 42% investigated different types of aquaculture including raft, cage or longline in combination [33][34][35][47][48][49]53,54,[78][79][80][81][82][83][84][85][86]…”

Section: Development Of Research Interest Over Timementioning

confidence: 99%

“…The zoomed-in map (Figure 8) of East and Southeast Asia clearly shows that aquaculture is being studied along the entire Chinese coast. The hotspots here are Sansha Bay in Fujian Province (16 studies) [33][34][35]37,47,49,56,65,69,73,76,80,83,85,86,88], the offshore area around Dalian in Liaoning Province (13 studies) [37,39,49,50,53,[57][58][59][60]62,67,68,73], and Haizhou Bay in Jiangsu Province (9 studies) [31,32,55,61,63,66,[71][72][73]. Further site and local studies of aquaculture were carried out on the coast of Japan [59,70,93,106], on the south coast of South Korea [81,82], the south coast of Vietnam [43,…”

Section: Areas Of Investigationmentioning

confidence: 99%

“…Only nine authors used multispectral data in combination with radar data as the basis for their studies. Of these nine, seven dealt with aquaculture [46,53,65,66,73,74,79] and two with platforms [30,94]. A total of 47 of the 89 studies (53%) dealt with the detection of offshore infrastructure based on multispectral satellite data.…”

Section: Employed Remote Sensing Sensorsmentioning

confidence: 99%

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Marine Infrastructure Detection with Satellite Data—A Review

Spanier,

Kuenzer

2024

Remote Sensing

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A rapid development of marine infrastructures can be observed along the global coasts. Offshore wind farms, oil and gas platforms, artificial islands, aquaculture, and more, are being constructed without a proper quantification of these human activities. Therefore, effective monitoring is required to maintain transparency towards environmental standards, marine resource management, inventorying objects, and global security. This study reviews remote sensing-based approaches to offshore infrastructure detection over the past 12 years. We analyzed 89 studies from over 30 scientific journals, highlighting spatial and temporal trends, methodological approaches, and regional and thematic research foci. Our results show a significant increase in research interest, especially since 2019. Asia, and especially China, is the predominant focus region in terms of first authorship, funding, and areas of investigation. Aquaculture is the most studied infrastructure, followed by platforms, offshore wind farms, and artificial islands. Gaofen, Sentinel, and Landsat are the most used satellite sensors for detection. The apparent shift towards automated detection methods, especially Deep Learning algorithms, reflects advances in computer vision. This study highlights the key role of earth observation in the field of off-shore infrastructure detection, which can contribute towards outlining effective monitoring practices for marine activities, as well as highlighting important knowledge gaps.

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Section: Methods Usedmentioning

confidence: 99%

Section: Development Of Research Interest Over Timementioning

confidence: 99%

Section: Areas Of Investigationmentioning

confidence: 99%

Section: Employed Remote Sensing Sensorsmentioning

confidence: 99%

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Marine Infrastructure Detection with Satellite Data—A Review

Spanier,

Kuenzer

2024

Remote Sensing

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Deep-Learning-Based Marine Aquaculture Zone Extractions From Dual-Polarimetric SAR Imagery

Chen,

2024

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

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Efficient monitoring of marine aquaculture zones 1 (MAZs) is crucial for facilitating coastal resource management. 2 To achieve this, we developed a specialized deep convolutional 3 neural network tailored for extracting MAZs from Synthetic 4 Aperture Radar (SAR) imagery, integrating prior analytical 5 knowledge of MAZ imaging features. 47 Sentinel-1 dual-polarized 6 (VV and VH) SAR images spanning 2016-2023 in China's Subei 7 Sandbanks along the Yellow Sea coast were collected due to 8 appropriate tidal level and acquired time. We first compre-9 hensively analyzed of Normalized Radar Cross Section (NRCS) 10 values for MAZs under varying tidal levels and aquaculture 11 facility structures. Rising tide-induced submergence resulted in 12 a significant mean NRCS reduction of 7.01 dB (VV) and 4.54 dB 13 (VH), causing MAZ signals to resemble seawater. Additionally, 14 during low tide, volume scattering from the net screen on the 15 aquaculture rafts increased VH-polarized image recognizability, 16 with a smaller NRCS overlap (64%) between MAZs and tidal 17 flats compared to VV-polarized images. Hence, VH-polarized 18 images taken during low tide with intact aquaculture facilities 19 were selected for dataset construction due to their reliability in 20 characterizing MAZs. Building upon the classical U-Net frame-21 work, we introduced four modifications informed by our imaging 22 characteristics analysis to enhance the model's performance. 23 Testing experiments demonstrated an impressive F1-score of 24 94.77%, highlighting the effectiveness of incorporating prior 25 knowledge into refining deep learning models. Applying the 26 model to SAR images from 2016 to 2023 revealed concentrated 27 MAZs in the relatively flat southeastern Subei Sandbanks, with a 28 noticeable scale decline post-2021 resulting in a 67.65% reduction 29 over the years. 30 Index Terms-synthetic aperture radar, Sentinel-1, imaging 31 characteristics, deep convolution neural networks, marine aqua-32 culture zones. 33 I. INTRODUCTION 34 M ARINE aquaculture is crucial and steadily expanding 35 worldwide [1]. In 2020, marine aquaculture production 36 reached 33.6 million tons, serving as a substantial source 37

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RSC-APMN: Random Sea Condition Adaptive Perception Modulating Network for SAR-Derived Marine Aquaculture Segmentation

Fan,

Deng

2024

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

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A Deep Learning Method for Offshore Raft Aquaculture Extraction Based on Medium-Resolution Remote Sensing Images

Cited by 5 publications

References 51 publications

Marine Infrastructure Detection with Satellite Data—A Review

Marine Infrastructure Detection with Satellite Data—A Review

Deep-Learning-Based Marine Aquaculture Zone Extractions From Dual-Polarimetric SAR Imagery

RSC-APMN: Random Sea Condition Adaptive Perception Modulating Network for SAR-Derived Marine Aquaculture Segmentation

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