| 图像自动识别技术在胶州湾浮游动物生态学研究中的应用 |
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| 引用本文： | 孙晓霞,孙 松,王世伟,刘梦坛,赵永芳.图像自动识别技术在胶州湾浮游动物生态学研究中的应用[J].海洋与湖沼,2011,42(5):647-653. |
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| 作者姓名： | 孙晓霞 孙 松 王世伟 刘梦坛 赵永芳 |
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| 作者单位： | 山东胶州湾海洋生态系统国家野外科学观测研究站;山东胶州湾海洋生态系统国家野外科学观测研究站;中国科学院海洋研究所 海洋生态与环境科学重点实验室;山东胶州湾海洋生态系统国家野外科学观测研究站;山东胶州湾海洋生态系统国家野外科学观测研究站;山东胶州湾海洋生态系统国家野外科学观测研究站 |
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| 基金项目： | 中国科学院知识创新工程重要方向项目群项目, KZCX2-YW-Q07-01 号; 国家“973”项目, 2011CB403603 号; 国家自然科学基金项目, 40876083 号, 40631008 号; 国家“973”项目, 2006CB400606 号; 国家海洋局公益项目, 200805042 号。 |
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| 摘 要： | 结合Zooscan扫描技术与ZooProcess分析与图像自动识别方法,进行了胶州湾浮游动物图像自动识别的研究。通过对胶州湾2009年浮游动物样品进行标准化扫描,随机选取不同类群的浮游动物图像,建立胶州湾浮游动物图像培训数据库并进行性能验证,表明对胶州湾绝大部分类群,图像识别的准确率可以达到80%以上,且误判率低于20...
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| 关 键 词： | 浮游动物 图像 自动识别 胶州湾 |
| 收稿时间： | 2011/1/21 0:00:00 |
| 修稿时间： | 2011/6/26 0:00:00 |
APPLICATION OF AUTOMATED IMAGE IDENTIFICATION IN ZOOPLANKTON ECOLOGY STUDIES IN THE JIAOZHOU BAY |
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| SUN Xiao-Xi,SUN Song,WANG Shi-Wei,LIU Meng-Tan and ZHAO Yong-Fang.APPLICATION OF AUTOMATED IMAGE IDENTIFICATION IN ZOOPLANKTON ECOLOGY STUDIES IN THE JIAOZHOU BAY[J].Oceanologia Et Limnologia Sinica,2011,42(5):647-653. |
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| Authors: | SUN Xiao-Xi SUN Song WANG Shi-Wei LIU Meng-Tan and ZHAO Yong-Fang |
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| Institution: | Jiaozhou Bay Marine Ecosystem Research Station, Institute of Oceanology, Chinese Academy of Sciences;Jiaozhou Bay Marine Ecosystem Research Station, Institute of Oceanology, Chinese Academy of Sciences; Key Laboratory of Marine Ecology and Environmental Sciences, Institute of Oceanology, Chinese Academy of Sciences;Jiaozhou Bay Marine Ecosystem Research Station, Institute of Oceanology, Chinese Academy of Sciences;Jiaozhou Bay Marine Ecosystem Research Station, Institute of Oceanology, Chinese Academy of Sciences;Jiaozhou Bay Marine Ecosystem Research Station, Institute of Oceanology, Chinese Academy of Sciences |
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| Abstract: | Zooplankton plays an important role in the marine ecosystem. How to rapidly identify zooplankton species is a key problem in zooplankton ecology studies. Automated zooplankton image identification technique is a rapid and standard method developed in recent years. However, this technique has not been used efficiently in zooplankton research in China. By combing the approaches including Zooscan, Zooprocess, and Plankton Identifier, we used the automated image identification method in the Jiaozhou Bay for the first time. A learning set of Jiaozhou Bay zooplankton images were set up according to the dominant zooplankton composition. Results of the performance test indicated that the recall was higher than 80%, and contamination was lower than 20% for most zooplankton groups. For the groups of Copepod, Chaetognath, Noctiluca, and Euphausia, the recall was higher than 90%. When comparing the results obtained from both automated and manual identification, we found that there was significant correlation between the two methods among the five dominant
groups, especially for the group of Copepod and Chaetognath, and the values of R2 reached 0.96 and 0.75, respectively. The automated analysis was further used for the study of biovolume and size spectra of zooplankton, which improved that the automated image identification was very useful for zooplankton ecological study and long term change research in the Jiaozhou Bay and other coastal ecosystems. |
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| Keywords: | Zooplankton Image Automated identification Jiaozhou Bay |
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