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A mathematical morphology based scale space method for the mining of linear features in geographic data

机译：基于数学形态学的尺度空间方法，用于挖掘地理数据中的线性特征

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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.
机译：本文提出了一种用于发现空间数据库中线性和近线性特征的空间数据挖掘方法MCAMMO及其扩展L_MCAMMO。 L_MCAMMO可以分为两个基本步骤：首先，通过MCAMMO找到最合适的重新分段尺度，这是一种具有数学形态学运算符的尺度空间方法;其次，将按此比例尺划分的分割结果重新分割，以获得最终的线性皮带。这些步骤本质上是一个多尺度的二值图像分割过程，如果我们将每个连接的组件下的点视为一个聚类，也可以将它们视为分层聚类。最终的聚类数是生存（相对不是绝对）最大尺度范围的聚类，最先实现此聚类数的聚类是最合适的分割方法。通常，MCAMMO的优点尤其是L_MCAMMO的优点是：无需预先指定簇的数量，少量的简单输入，能够提取具有任意形状的簇并且对噪声具有鲁棒性。在中国地震带开采中的实际实验证明了该方法的有效性。

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