A simple estimation the number of classes in satellite imagery

Kitti Koonsanit, C. Jaruskulchai
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引用次数: 4

Abstract

Clustering is a popular tool for exploratory data analysis, such as K-means and Fuzzy C-mean. A simple estimation the number of classes for segmented areas (K) in satellite imagery application is often needed in advance as an input parameter to the K-means algorithm. In this paper, a method has been developed to estimate the number of classes for segmented areas in satellite imagery clustering application using an image processing technique based on the co-occurrence matrix technique. The proposed method was tested using data from known the number of classes with satellite imagery. The results from the tests confirm the effectiveness of the proposed method in finding the estimation the number of classes and compared with ground truth data.
卫星图像中类别数量的简单估计
聚类是一种流行的探索性数据分析工具,如k均值和模糊c均值。在卫星图像应用中,通常需要预先对分割区域的类数(K)进行简单估计,作为K-means算法的输入参数。本文提出了一种基于共现矩阵的图像处理技术在卫星图像聚类应用中对分割区域进行类数估计的方法。利用卫星图像中已知班级数量的数据对所提出的方法进行了测试。实验结果证实了该方法在寻找估计类数方面的有效性,并与地面真值数据进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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