Cite this paper:
LIU Yueming, YANG Xiaomei, WANG Zhihua, LU Chen, LI Zhi, YANG Fengshuo. Aquaculture area extraction and vulnerability assessment in Sanduao based on richer convolutional features network model[J]. HaiyangYuHuZhao, 2019, 37(6): 1941-1954

Aquaculture area extraction and vulnerability assessment in Sanduao based on richer convolutional features network model

LIU Yueming1,3, YANG Xiaomei1,3,4, WANG Zhihua1, LU Chen1,3, LI Zhi2,3, YANG Fengshuo1,3
1 State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;
2 State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China;
3 University of Chinese Academy of Sciences, Beijing 100049, China;
4 Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
Sanduao is an important sea-breeding bay in Fujian, South China and holds a high economic status in aquaculture. Quickly and accurately obtaining information including the distribution area, quantity, and aquaculture area is important for breeding area planning, production value estimation, ecological survey, and storm surge prevention. However, as the aquaculture area expands, the seawater background becomes increasingly complex and spectral characteristics differ dramatically, making it difficult to determine the aquaculture area. In this study, we used a high-resolution remote-sensing satellite GF-2 image to introduce a deep-learning Richer Convolutional Features (RCF) network model to extract the aquaculture area. Then we used the density of aquaculture as an assessment index to assess the vulnerability of aquaculture areas in Sanduao. The results demonstrate that this method does not require land and water separation of the area in advance, and good extraction can be achieved in the areas with more sediment and waves, with an extraction accuracy >93%, which is suitable for large-scale aquaculture area extraction. Vulnerability assessment results indicate that the density of aquaculture in the eastern part of Sanduao is considerably high, reaching a higher vulnerability level than other parts.
Key words:    aquaculture area|vulnerability assessment|Richer Convolutional Features (RCF) network model|deep learning|high-resolution remote sensing   
Received: 2018-09-25   Revised: 2019-03-18
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