A mathematical morphology based scale space method for the mining of linear features in geographic data
文献类型：EI期刊论文
| 作者 | Pei Tao |
| 发表日期 | 2006 |
| 关键词 | Data mining Data reduction Hierarchical systems Image analysis Image segmentation Linear systems Mathematical morphology |
| 英文摘要 | This paper presents a spatial data mining method MCAMMO and its extension L_MCAMMO designed for discovering linear and near linear features in spatial databases. L_MCAMMO can be divided into two basic steps: first, the most suitable re-segmenting scale is found by MCAMMO, which is a scale space method with mathematical morphology operators; second, the segmented result at this scale is re-segmented to obtain the final linear belts. These steps are essentially a multi-scale binary image segmentation process, and can also be treated as hierarchical clustering if we view the points under each connected component as one cluster. The final number of clusters is the one which survives (relatively, not absolutely) the longest scale range, and the clustering which first realizes this number of clusters is the most suitable segmentation. The advantages of MCAMMO in general and L_MCAMMO in particular, are: no need to pre-specify the number of clusters, a small number of simple inputs, capable of extracting clusters with arbitrary shapes, and robust to noise. The effectiveness of the proposed method is substantiated by the real-life experiments in the mining of seismic belts in China. © 2005 Springer Science+Business Media, Inc. |
| 出处 | Data Mining and Knowledge Discovery
 |
| 卷 | 12期:1页:97-118 |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/24547]  |
| 专题 | 地理科学与资源研究所_历年回溯文献 |
| 推荐引用方式 GB/T 7714 | Pei Tao. A mathematical morphology based scale space method for the mining of linear features in geographic data. 2006. |
入库方式： OAI收割
来源：地理科学与资源研究所
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