Paper Info
Title
A Mathematical Morphology Based Scale Space Method for the Mining of Linear Features in Geographic Data
Abstract
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.
Year
DOI
Venue
2006
10.1007/s10618-005-0021-7
Data Min. Knowl. Discov.
Keywords
DocType
Volume
Mathematical Morphology,Scale Space Theory,Clustering,Spatial Data Mining,Linear Belt,Seismic Belt
Journal
12
Issue
ISSN
Citations 
1
1384-5810
4
PageRank 
References 
Authors
0.49
12
5
Name
Order
Citations
PageRank
Min Wang1162.53
Yee Leung2208196.44
Chenhu Zhou340.49
Tao Pei422223.59
Jian-Cheng Luo59920.75