Review of the Accuracy of Satellite Remote Sensing Techniques in Identifying Coastal Aquaculture Facilities

被引:1
作者
Chen, Ao [1 ]
Lv, Zehua [1 ,2 ,3 ]
Zhang, Junbo [1 ,2 ]
Yu, Gangyi [1 ]
Wan, Rong [1 ,2 ]
机构
[1] Shanghai Ocean Univ, Colloge Marine Living Resource Sci & Management, Shanghai 201306, Peoples R China
[2] Shanghai Ocean Univ, Natl Engn Res Ctr Ocean Fisheries, Shanghai 201306, Peoples R China
[3] Natl Engn Res Ctr Oceanic Fisheries, Zhoushan Branch, Zhoushan 316014, Peoples R China
关键词
satellite remote sensing image; coastal facility fishery area; target recognition; information extraction; EXTRACTION;
D O I
10.3390/fishes9020052
中图分类号
S9 [水产、渔业];
学科分类号
0908 ;
摘要
The predominant form of aquaculture is the facility fishery, which is also subject to significant impacts from marine disasters. Conducting research on the extraction of facility fishery areas based on remote sensing technology is crucial to efficiently comprehending the configuration of coastal culture patterns and to establishing scientifically sound plans for managing and administering these areas. The extensive dispersion of facility fishery areas in coastal regions poses a challenge to the conduction of comprehensive field surveys. The utilization of satellite remote sensing images for information extraction has emerged as a significant area of research in the fields of coastal fishery and ecological environment. This study provides a systematic description of the current research status of coastal fishery area extraction methods using remote sensing technology from 2000 to 2022 reported in the literature. The methods discussed include the visual interpretation method, image element-based classification, object-based classification, supervised classification, unsupervised classification, and neural network classification. The extraction accuracy of each method in the coastal facility fishery area is evaluated, and the advantages and disadvantages of these methods, as well as their limitations and existing problems, are analyzed in detail, to construct a reference framework for the investigation of the high-precision extraction of facility fishery areas from satellite remote sensing images.
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页数:16
相关论文
共 62 条
  • [1] Comparative Analysis of the Sensitivity of SAR Data in C and L Bands for the Detection of Irrigation Events
    Bazzi, Hassan
    Baghdadi, Nicolas
    Charron, Francois
    Zribi, Mehrez
    [J]. REMOTE SENSING, 2022, 14 (10)
  • [2] Remote Sensing Monitoring of Durum Wheat under No Tillage Practices by Means of Spectral Indices Interpretation: A Preliminary Study
    Calcagno, Federico
    Romano, Elio
    Furnitto, Nicola
    Jamali, Arman
    Failla, Sabina
    [J]. SUSTAINABILITY, 2022, 14 (22)
  • [3] Building detection methods from remotely sensed images
    Chandra, Naveen
    Vaidya, Himadri
    [J]. CURRENT SCIENCE, 2022, 122 (11): : 1252 - 1267
  • [4] Edge detection of remote sensing image based on Gru?nwald-Letnikov fractional difference and Otsu threshold
    Chen, Chao
    Kong, Hua
    Wu, Bin
    [J]. ELECTRONIC RESEARCH ARCHIVE, 2023, 31 (03): : 1287 - 1302
  • [5] Review of agricultural spraying technologies for plant protection using unmanned aerial vehicle (UAV)
    Chen, Haibo
    Lan, Yubin
    Fritz, Bradley K.
    Hoffmann, W. Clint
    Liu, Shengbo
    [J]. INTERNATIONAL JOURNAL OF AGRICULTURAL AND BIOLOGICAL ENGINEERING, 2021, 14 (01) : 38 - 49
  • [6] Research on a novel extraction method using Deep Learning based on GF-2 images for aquaculture areas
    Cheng, Bo
    Liang, Chenbin
    Liu, Xunan
    Liu, Yueming
    Ma, Xiaoxiao
    Wang, Guizhou
    [J]. INTERNATIONAL JOURNAL OF REMOTE SENSING, 2020, 41 (09) : 3575 - 3591
  • [7] [程博 Cheng Bo], 2018, [遥感技术与应用, Remote Sensing Technology and Application], V33, P296
  • [8] Chu J., 2021, Remote Sens. Technol. Appl, V27, P941, DOI [10.11873/J.ISSN.1004-0323.2012.6.941, DOI 10.11873/J.ISSN.1004-0323.2012.6.941]
  • [9] Extracting Raft Aquaculture Areas from Remote Sensing Images via an Improved U-Net with a PSE Structure
    Cui, Binge
    Fei, Dong
    Shao, Guanghui
    Lu, Yan
    Chu, Jialan
    [J]. REMOTE SENSING, 2019, 11 (17)
  • [10] A Convolutional Neural Network for Coastal Aquaculture Extraction from High-Resolution Remote Sensing Imagery
    Deng, Jinpu
    Bai, Yongqing
    Chen, Zhengchao
    Shen, Ting
    Li, Cong
    Yang, Xuan
    [J]. SUSTAINABILITY, 2023, 15 (06)