计算机工程与应用2018，Vol.54Issue(1)：196-203,8.DOI:10.3778/j.issn.1002-8331.1706-0114
改进蚁群算法及其在高光谱影像分类中的研究
Improved ant colony optimization algorithm and its research on hyperspectral image classification
摘要
Abstract
An Improved Binary Ant Colony Optimization(IBACO)algorithm is proposed, which combined with the initial heuristic information of Genetic Algorithm(GA)and the global optimization ability of Binary Ant Colony Optimi-zation(BACO)algorithm to overcome the high dimensionality of hyperspectral image. In the proposed method, the results of GA are utilized as the initial heuristic information of BACO, and the ant path selection mechanism is improved to enhance the global optimization ability. On the other hand, texture feature is utilized to make full use of the spectral and spatial information, and the combination of spectral and texture can obtain higher classification accuracy. Experimental results illustrate that the search ability of IBACO is better than GA, ACO, BACO. Meanwhile, the classification accuracy by using the proposed technique has reached 95.63%. In all, the proposed method can effectively enhance the classification efficiency and classification accuracy.关键词
高光谱影像分类/改进二进制蚁群算法/波段选择/光谱特征/纹理特征Key words
hyperspectral image classification/Improved Binary Ant Colony Optimization(IBACO)algorithm/band selection/spectral feature/texture feature分类
信息技术与安全科学引用本文复制引用
王偲晗,万幼川,王明威,高雄..改进蚁群算法及其在高光谱影像分类中的研究[J].计算机工程与应用,2018,54(1):196-203,8.基金项目
国家重点专项(No.2016YFC0600210) （No.2016YFC0600210）
国家科技支撑计划项目(No.2014BAL05B07) （No.2014BAL05B07）
测绘遥感信息工程国家重点实验室开放基金(No.13R04). （No.13R04）
