数字河口平台建设关键技术研究
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摘要
信息技术的飞速发展改变了和改变着我们的生活方式,我们的生活理念,冲击着社会的各个领域,许多领域和行业已经发生和正在发生巨大的变革,这是一个信息的时代。在当今信息社会,人们对信息的需求量越来越大,特别是在科学研究中,多学科的交叉和融合对数据集成、处理和共享提出了更高的要求,仅靠传统的方法越来越难以满足人们的需要。
纸质地图和海洋调查数据都是重要的数据来源,其数据类型纷繁复杂,数量也非常大,数据集成和处理工作非常繁重,仅靠传统的手工操作难以完成该任务,本文通过对地图自动识别与矢量化方法和海洋调查数据处理方法的改进,大大节约了处理时间,加快了数据集成和处理的进程,对于数据共享具有重要意义。
海洋数据的处理方法与一般数据处理方法不同,具有其独特性,本文对数据的处理并不局限于已有文献采用的方法,而是根据海洋数据本身的特点,通过分析其内在特征,充分利用信息技术最新的研究成果,在已有研究的基础上加以综合和改进,提出了自己的观点和方法,本文的研究成果主要包括:
1在等值线自动识别方面,首次采用了神经网络方法对图文进行分离,取得了较好的分离效果;并提出了基于非线性埃尔米特插值和能量函数相结合的等值线识别方法,跟踪步长增大,克服了采用直线段跟踪等值线步长小的缺点,判识等值线所依据的信息量更大,符合等值线整体识别的发展趋势。
2在CTD数据处理方面,针对CTD数据的特点,设计了独特的粗大误差处理流程和处理方法,解决了常规方法效率低,操作繁琐的特点。
3结合海洋沉积学领域端元分析模型,开发了端元分解算法,并首次利用该模型对东海陆架沉积物粒度数据进行了研究和分析,揭示了其中蕴含的大量信息。
This is a big era with big change and big challenge, Information Technology has remodeled our life, our thought, and has greatly influenced each domain of whole society, this is an era of information. More and more Information is demanded in the progress, especially in scientific research activity, multi-discipline integration research call for deep integration,process and share of multi-source Data, the traditional information process methods can’t satisfies us now.
Paper map and ocean survey data are important data sources nowadays, its datatype is various and complex, its quantity is also large, the data integration and process job is tedious and difficult only depend on manual process. In this paper, through the improvement of paper map automatic vectorization algorithm and ocean survey data process method, the data integration and process is quickened, large amounts of time is saved, this is very helpful to the share of data.
The process method of ocean data have its own feature, and is very different from ordinary data process method, in this paper, we are not restricted of theory, method and algorithms that proved effectively, we put our focus on the ocean data’s feature, through analysis of ocean data’s essential characteristics and application of up to time computer technology achievement, new methods are recommended. Main contributions of the dissertation include:
1 In the isoline automatic vectorization domain, a novel picture-text separation algorithm which use neural network to separate text from isoline is present, the process result is fine; and we present a new isoline nonlinear Hermite interpolation tracing method, the method use nonlinear Hermite interpolation and energy function to optimize the position of key points, it acquire a big tracing step, overcome the linear interpolation’s shortcome, the information used to pin the key points is more than linear interpolation method, this is accord with the holistic automatic vectorization tendency of isolines.
2 In the CTD data process domain, after analyse of CTD data’s characteristics, we present a unique CTD wild data eliminatation method, the process efficiency of CTD data is greatly improved.
3 Finally, in the marine sedimentology data process domain, we give a End Member unmixing program, in the study of the sediment from East China Sea shelf, we get many information hidden in the grain size data using this program.
纸质地图和海洋调查数据都是重要的数据来源,其数据类型纷繁复杂,数量也非常大,数据集成和处理工作非常繁重,仅靠传统的手工操作难以完成该任务,本文通过对地图自动识别与矢量化方法和海洋调查数据处理方法的改进,大大节约了处理时间,加快了数据集成和处理的进程,对于数据共享具有重要意义。
海洋数据的处理方法与一般数据处理方法不同,具有其独特性,本文对数据的处理并不局限于已有文献采用的方法,而是根据海洋数据本身的特点,通过分析其内在特征,充分利用信息技术最新的研究成果,在已有研究的基础上加以综合和改进,提出了自己的观点和方法,本文的研究成果主要包括:
1在等值线自动识别方面,首次采用了神经网络方法对图文进行分离,取得了较好的分离效果;并提出了基于非线性埃尔米特插值和能量函数相结合的等值线识别方法,跟踪步长增大,克服了采用直线段跟踪等值线步长小的缺点,判识等值线所依据的信息量更大,符合等值线整体识别的发展趋势。
2在CTD数据处理方面,针对CTD数据的特点,设计了独特的粗大误差处理流程和处理方法,解决了常规方法效率低,操作繁琐的特点。
3结合海洋沉积学领域端元分析模型,开发了端元分解算法,并首次利用该模型对东海陆架沉积物粒度数据进行了研究和分析,揭示了其中蕴含的大量信息。
This is a big era with big change and big challenge, Information Technology has remodeled our life, our thought, and has greatly influenced each domain of whole society, this is an era of information. More and more Information is demanded in the progress, especially in scientific research activity, multi-discipline integration research call for deep integration,process and share of multi-source Data, the traditional information process methods can’t satisfies us now.
Paper map and ocean survey data are important data sources nowadays, its datatype is various and complex, its quantity is also large, the data integration and process job is tedious and difficult only depend on manual process. In this paper, through the improvement of paper map automatic vectorization algorithm and ocean survey data process method, the data integration and process is quickened, large amounts of time is saved, this is very helpful to the share of data.
The process method of ocean data have its own feature, and is very different from ordinary data process method, in this paper, we are not restricted of theory, method and algorithms that proved effectively, we put our focus on the ocean data’s feature, through analysis of ocean data’s essential characteristics and application of up to time computer technology achievement, new methods are recommended. Main contributions of the dissertation include:
1 In the isoline automatic vectorization domain, a novel picture-text separation algorithm which use neural network to separate text from isoline is present, the process result is fine; and we present a new isoline nonlinear Hermite interpolation tracing method, the method use nonlinear Hermite interpolation and energy function to optimize the position of key points, it acquire a big tracing step, overcome the linear interpolation’s shortcome, the information used to pin the key points is more than linear interpolation method, this is accord with the holistic automatic vectorization tendency of isolines.
2 In the CTD data process domain, after analyse of CTD data’s characteristics, we present a unique CTD wild data eliminatation method, the process efficiency of CTD data is greatly improved.
3 Finally, in the marine sedimentology data process domain, we give a End Member unmixing program, in the study of the sediment from East China Sea shelf, we get many information hidden in the grain size data using this program.
引文
1. http://mds.coi.gov.cn
2. http://w3k.gkss.de/loiczbasins
3. http://www.loicz.org
4. http://www.mapgis.com.cn
5. http://www.ngdc.noaa.gov/wdc
6. http://www.nynjcoast.org/VirtualEstuary
7. http://www.ouc.edu.cn/diqiu/973
8. http://www.geostar.com.cn
9. http://www.geoway.com.cn
10. http://www.seabird.com
11. http://www.yrdsd.com
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