| 时空一体化框架下时空异常探测 |
| |
| 引用本文： | 刘启亮,邓敏,王佳璆,彭思岭,梅小明,赵玲.时空一体化框架下时空异常探测[J].遥感学报,2011,15(3):457-474. |
| |
| 作者姓名： | 刘启亮 邓敏 王佳璆 彭思岭 梅小明 赵玲 |
| |
| 作者单位： | 中南大学,测绘与国土信息工程系,湖南,长沙,410083 |
| |
| 基金项目： | 国家高技术研究发展计划(863)(编号：2009AA12Z206)；地理空间信息工程国家测绘局重点实验室开放基金重点项目(编号：
200916)；江苏省资源环境信息工程重点实验室(中国矿业大学)开放基金项目(编号：20080101、JS200901)；江西省数字国土重点实验室开放基金项
目(编号：DLLJ201005)。 |
| |
| 摘 要： | ：提出一种时空一体化的时空异常探测方法，首先基于时空统计学与聚类分析构建一体化时空邻近域。进而，
发展兼顾时空相关与异质性的时空异常度量方法。最后，采用一种3步骤的策略探测时空异常。应用本文方法探测中国
陆地区域33年(1970年—2002年)的年平均气温时空数据中的时空异常，探测结果具有较好的可靠性，反映时空数据的时
空一体化特征。同时，对时空异常的产生机理与实际意义进行分析和解释。
|
| 关 键 词： | 时空异常探测，时空邻近域，时空统计学，聚类分析 |
| 收稿时间： | 2010/1/18 0:00:00 |
| 修稿时间： | 6/2/2010 12:00:00 AM |
Spatio-Temporal outliers detection within the space-time framework |
| |
| LIU Qiliang,DENG Min,WANG Jiaqiu,PENG Siling,MEI Xiaoming and ZHAO Ling.Spatio-Temporal outliers detection within the space-time framework[J].Journal of Remote Sensing,2011,15(3):457-474. |
| |
| Authors: | LIU Qiliang DENG Min WANG Jiaqiu PENG Siling MEI Xiaoming and ZHAO Ling |
| |
| Institution: | Department of Surveying and Geo-informatics, Central South University, Hunan Changsha 410083, China;Department of Surveying and Geo-informatics, Central South University, Hunan Changsha 410083, China;Department of Surveying and Geo-informatics, Central South University, Hunan Changsha 410083, China;Department of Surveying and Geo-informatics, Central South University, Hunan Changsha 410083, China;Department of Surveying and Geo-informatics, Central South University, Hunan Changsha 410083, China;Department of Surveying and Geo-informatics, Central South University, Hunan Changsha 410083, China |
| |
| Abstract: | A novel spatio-temporal outlier detection method within the space-time framework is proposed in this paper. Firstly,
a unifi ed framework is developed for constructing spatio-temporal neighborhood, which is based on the space-time statistics and
clustering analysis. Then, a spatio-temporal outlier measure involving space-time autocorrelation and heterogeneity is presented.
Finally, a tree-step strategy is utilized to detect spatio-temporal outliers. Our method is employed to detect spatio-temporal outliers
in Chinese annual temperature database (1970-2002). A meaningful analysis of the spatio-temporal outliers is also provided.
Key words: spatio-temporal outlier detection, spatio-temporal neighborhood, space-time statistics, clustering analysis |
| |
| Keywords: | spatio-temporal outlier detection spatio-temporal neighborhood space-time statistics clustering analysis |
| 本文献已被 CNKI 万方数据 等数据库收录！ |
| 点击此处可从《遥感学报》浏览原始摘要信息 |
| 点击此处可从《遥感学报》下载免费的PDF全文 |
|
