基于猫群算法的高光谱图像森林类型识别  

Forest Type Clustering Based on Cat Swarm Algorithm and Hyperspectral Image

作　　者：李琰[1] 王立海[1] 邢艳秋[1] 

机构地区：[1]东北林业大学,哈尔滨150040

出　　处：《东北林业大学学报》2015年第7期110-115,共6页Journal of Northeast Forestry University

基　　金：国家林业局"948"项目(2014-4-78);国家自然科学基金面上项目(41171274)

摘　　要：以吉林省汪清林业局为研究区,通过猫群位置寻优的过程对阔叶林、针叶林和混交林进行聚类分析。结果表明:森林类型区分精度达到83.5%,Kappa系数0.793,与传统高光谱聚类方法相比,能较好的识别森林类型。In Wangqing Forestry Bureau of Jilin Province, by location optimization process of cat swarm algorithm, we clustered the forest types between broad-leaved forest, coniferous forest and mixed forest with better effect compared with traditional hyperspectral clustering method, and the classification accuracy of forest type clustering was 83.5%, and Kappa coefficient was 0.793.

关 键 词：猫群优化算法 高光谱图像 森林类型 

分 类 号：S771.8[农业科学—森林工程]