High Resolution Satellite Classification with Graph Cut Algorithms

被引：0
|
作者：
Lopez, Adrian A. [1 ]
Malpica, Jose A. [1 ]
机构：
[1] Alcala Univ, Dept Math, Sch Geodesy & Cartog, Madrid 28871, Spain
来源：
ADVANCES IN VISUAL COMPUTING, PT II, PROCEEDINGS | 2008年 / 5359卷
关键词：
Graph cuts; k-means; high resolution imagery; unsupervised classification;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, an unsupervised classification technique is proposed for high resolution satellite imagery. The approach uses graph cuts to improve the k-means algorithm, as graph cuts introduce spatial domain information of the image that is lacking in the k-means. High resolution satellite imagery, IKO-NOS, and SPOT-5 have been evaluated by the proposed method, showing that graph cuts improve k-means results, which in turn show coherent and continually spatial cluster regions that could be useful for cartographic classification.
引用
收藏
页码：105 / 112
页数：8
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