计算机工程与应用2019，Vol.55Issue(6)：151-159,9.DOI:10.3778/j.issn.1002-8331.1809-0052
基于蜂群k-means算法的遥感图像聚类应用研究
Research on Remote Sensing Image Clustering Based on Bee Colony k-means Algorithm
摘要
Abstract
Acquiring labeled data for the training a classifier is very difficult, times consuming and expensive in the area of remote sensing. Many semi-supervised techniques have been developed and explored for the classification of remote sensing images with limited number of labeled samples. In this paper, a new unsupervised clustering algorithm is pro-posed by combining k-means and bee colony algorithm. Features of remote sensing images are extracted by Gray Level Co-occurrence Matrix(GLCM)and wavelet transform, and then k-means clustering of feature dataset is performed. The initial clustering center is generated by the maximum-minimum product-neighborhood averaging method. The new swarm algorithm and k-means algorithm are alternately implemented to achieve remote sensing image clustering. With the com-parison experiment of the UCI dataset and the Liangshui National Nature Reserve remote sensing image data, the algo-rithm has high clustering accuracy and meets the application requirements of remote sensing image clustering.关键词
遥感图像/k-means聚类/蜂群算法Key words
remote sensing/k-means clustering/bee colony algorithm分类
信息技术与安全科学引用本文复制引用
李艳娟,牛梦婷,李林辉..基于蜂群k-means算法的遥感图像聚类应用研究[J].计算机工程与应用,2019,55(6):151-159,9.基金项目
教育部协同育人项目(No.201702185053) （No.201702185053）
河北省高等学校青年拔尖人才计划项目(No.BJ2017105) （No.BJ2017105）
河北省科技计划支撑项目(No.16222101D) （No.16222101D）
石家庄市重点研发项目(No.181230041A). （No.181230041A）
