基于小波特征和模拟退火的遥感图象快速聚类算法  

Fast Clustering Based on Spectral Wavelet Features Extraction and Simulated Annealing Algorithm for Multispectral Images

作　　者：王朝晖[1] 周佩玲[1] 

机构地区：[1]中国科学技术大学电子科学与技术系,合肥230026

出　　处：《中国图象图形学报（A辑）》2002年第12期1257-1262,共6页Journal of Image and Graphics

基　　金：安徽重点项目资助 ( 2 0 0 0 0 95 )

摘　　要：不同遥感对象 ,光谱曲线的突变点位置差异很大 ,不同尺度的小波变换可有效提取这些突变特征 ,在此基础上 ,用小波特征相关系数描述像素间的近似程度 ,取代一般聚类算法以欧氏距离为基础的聚类概念 ,聚类结果可准确反映遥感对象内容 .基于小波特征抽取和模拟退火的多光谱遥感图象快速聚类算法 ,通过扩展频段 ,增加特征点的个数以丰富类的特性 ;对空间数据进行均匀抽样产生聚类空间 ,采用模拟退火技术和逐步降低聚类规模的方法 ,快速实现全局最优的聚类中心 ;类内评价最优代表作为聚类中心 ,保证类特性的持续性和强壮性 ;而且解决了K- means聚类的参数选择问题 .最后采用 TM多光谱遥感图象进行参数分析和算法比较 ,验证了该算法分类快速准确 ,且参数控制灵活 。The differences of the spectral curves among different objects are so obvious and important to be extracted through the wavelet transform at different scale. The traditional clustering concepts based on Euclidean distance are also redefined by the wavelet feature correlation coefficient to accurately describe the content of the remotely sensed objects. The fast clustering algorithm for multispectral images based on wavelet feature and simulated annealing increases the number of characteristic points by expanding the spectral bands to enrich the feature of the classes; clustering space is formed by evenly sampled dots; furthermore, simulated annealing leads to the best class centers on the whole scope at a high speed by decreasing the clustering scale and temperature step by step; the class characters is remained strong and durative by choosing the best dot as the class center; it also resolves the initial parameter problem of K means algorithm. The experimental results of Mississippi Thematic Mapper images show that this clustering algorithm is more efficient than other ordinary clustering algorithms such as K means and ISODATA according to the clustering accuracy and speed. Therefore, there are fairly prosperous applications on multispectral images for this fast clustering based on spectral wavelet features extraction and simulated annealing algorithm.

关 键 词：遥感图象 聚类算法 多光谱图象 小波变换 模拟退火 图象处理 

分 类 号：TP751.1[自动化与计算机技术—检测技术与自动化装置]