关键词: 遥感图像, [k]-means聚类, 蜂群算法

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 proposed 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 comparison experiment of the UCI dataset and the Liangshui National Nature Reserve remote sensing image data, the algorithm has high clustering accuracy and meets the application requirements of remote sensing image clustering.

Key words: remote sensing, [k]-means clustering, bee colony algorithm