基于深度学习的无人机影像夜光藻赤潮提取方法
文献类型：期刊论文
| 作者 | 李敬虎1; 邢前国2,3 ; 郑向阳2; 李琳2; 王丽丽1
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| 刊名 | 计算机应用
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| 出版日期 | 2022 |
| 卷号 | 42期号:9页码:2969-2974 |
| 关键词 | 夜光藻 赤潮 无人机 深度学习 UNet++ 视频处理 |
| ISSN号 | 1001-9081 |
| 其他题名 | Noctiluca scintillans red tide extraction method from UAV images based on deep learning |
| 文献子类 | 期刊论文 |
| 英文摘要 | Aiming at the problems of low accuracy and poor real-time performance of Noctiluca scintillans red tide extraction in the field of satellite remote sensing,a Noctiluca scintillans red tide extraction method from Unmanned Aerial Vehicle(UAV)images based on deep learning was proposed. Firstly,the high-resolution RGB(Red-Green-Blue)videos collected by UAV were used as the monitoring data,the backbone network was modified to VGG-16(Visual Geometry Group- 16)and the spatial dropout strategy was introduced on the basis of the original UNet++ network to enhance the feature extraction ability and prevent the overfitting respectively. Then,the VGG-16 network pre-trained by using ImageNet dataset was applied to perform transfer learning to increase the network convergence speed. Finally,in order to evaluate the performance of the proposed method,experiments were conducted on the self-built red tide dataset Redtide-DB. The Overall Accuracy(OA),F1 score,and Kappa of the Noctiluca scintillans red tide extraction of the proposed method are up to 94.63%,0.955 2,0.949 6 respectively,which are better than those of three traditional machine learning methods - KNearest Neighbors(KNN),Support Vector Machine(SVM)and Random Forest(RF)as well as three typical semantic segmentation networks(PSPNet(Pyramid Scene Parsing Network),SegNet and U-Net). Meanwhile,the red tide images of different shooting equipment and shooting environments were used to test the generalization ability of the proposed method, and the corresponding OA,F1 score and Kappa are 97.41%,0.965 9 and 0.938 2,respectively,proving that the proposed method has a certain generalization ability. Experimental results show that the proposed method can realize the automatic accurate Noctiluca scintillans red tide extraction in complex environments,and provides a reference for Noctiluca scintillans red tide monitoring and research work. |
| 语种 | 中文 |
| CSCD记录号 | CSCD:7324361 |
| 源URL | [http://ir.yic.ac.cn/handle/133337/34168]  |
| 专题 | 烟台海岸带研究所_中科院海岸带环境过程与生态修复重点实验室 烟台海岸带研究所_海岸带信息集成与综合管理实验室 |
| 作者单位 | 1.鲁东大学信息与电气工程学院,山东烟台264025; 2.中国科学院烟台海岸带研究所,山东烟台264003; 3.中国科学院大学,北京100049 |
| 推荐引用方式 GB/T 7714 | 李敬虎,邢前国,郑向阳,等. 基于深度学习的无人机影像夜光藻赤潮提取方法[J]. 计算机应用,2022,42(9):2969-2974. |
| APA | 李敬虎,邢前国,郑向阳,李琳,&王丽丽.(2022).基于深度学习的无人机影像夜光藻赤潮提取方法.计算机应用,42(9),2969-2974. |
| MLA | 李敬虎,et al."基于深度学习的无人机影像夜光藻赤潮提取方法".计算机应用 42.9(2022):2969-2974. |
入库方式： OAI收割
来源：烟台海岸带研究所
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