Name
Abstract | ||
|---|---|---|
Detecting spatial outliers can help identify significant anomalies in spatial data sequences. In the field of meteorological data processing, spatial outliers are frequently associated with natural disasters such as tornadoes and hurricanes. Previous studies on spatial outliers mainly focused on identifying single location points over a static data frame. In this paper, we propose and implement a systematic methodology to detect and track regional outliers in a sequence of meteorological data frames. First, a wavelet transformation such as the Mexican Hat or Morlet is used to filter noise and enhance the data variation. Second, an image segmentation method, @l-connected segmentation, is employed to identify the outlier regions. Finally, a regression technique is applied to track the center movement of the outlying regions for consecutive frames. In addition, we conducted experimental evaluations using real-world meteorological data and events such as Hurricane Isabel to demonstrate the effectiveness of our proposed approach. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1016/j.ins.2006.09.013 | Inf. Sci. |
Keywords | Field | DocType |
hurricane isabel,real-world meteorological data,meteorological data processing,regional outlier,static data frame,l-connected segmentation,image segmentation method,meteorological data frame,data variation,spatial data sequence,spatial outlier,wavelet transform,image segmentation,wavelet,natural disaster,data processing,outlier detection,spatial data | Spatial analysis,Data mining,Data processing,Regression,Segmentation,Outlier,Image segmentation,Hurricane Isabel,Mathematics,Wavelet | Journal |
Volume | Issue | ISSN |
177 | 7 | 0020-0255 |
Citations | PageRank | References |
19 | 0.77 | 28 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chang-Tien Lu | 1 | 1097 | 115.77 |
| Yufeng Kou | 2 | 227 | 14.41 |
| Jiang Zhao | 3 | 78 | 10.02 |
| Li Chen | 4 | 77 | 8.25 |