【摘要】：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.