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...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.展开更多
针对边缘检测时提取的边缘特征粗糙问题,提出了改进的更丰富特征的边缘检测(Richer Convolutional Features for edge detection,RCF)算法。该算法利用跨层交叉融合的方法,以侧边输出的第3层作为基底分别与第1层、第5层的侧边输出图像...针对边缘检测时提取的边缘特征粗糙问题,提出了改进的更丰富特征的边缘检测(Richer Convolutional Features for edge detection,RCF)算法。该算法利用跨层交叉融合的方法,以侧边输出的第3层作为基底分别与第1层、第5层的侧边输出图像两两进行交叉融合,同时去掉了第3、4阶段的池化层,得到精确的边缘特征信息。在BSDS500数据集上对改进的RCF模型进行训练,实验结果表明,改进的RCF算法与原RCF算法相比F1值更高,提取效果更优秀。展开更多
基金Supported by the National Key Research and Development Program of China(No.2016YFC1402003)the National Natural Science Foundation of China(No.41671436)the Innovation Project of LREIS(No.O88RAA01YA)
文摘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.
文摘针对边缘检测时提取的边缘特征粗糙问题,提出了改进的更丰富特征的边缘检测(Richer Convolutional Features for edge detection,RCF)算法。该算法利用跨层交叉融合的方法,以侧边输出的第3层作为基底分别与第1层、第5层的侧边输出图像两两进行交叉融合,同时去掉了第3、4阶段的池化层,得到精确的边缘特征信息。在BSDS500数据集上对改进的RCF模型进行训练,实验结果表明,改进的RCF算法与原RCF算法相比F1值更高,提取效果更优秀。
