基于改进蚁群算法的无人机遥感影像森林类型分类  

Classification of Forest Types using UAV Remote Sensing Images Based on Improved Ant Colony Algorithm

作　　者：赵桂玲[1] 李鹏年 郭泉荣 谭茂林 Zhao Guiling;Li Pengnian;Guo Quanrong;Tan Maolin(School of Geomatics,Liaoning Technical University,Fuxin 123000,Liaoning,China)

机构地区：[1]辽宁工程技术大学测绘与地理科学学院,辽宁阜新123000

出　　处：《激光与光电子学进展》2022年第16期409-416,共8页Laser & Optoelectronics Progress

基　　金：辽宁省自然科学基金面上项目(2020-MS-303);辽宁省教育厅一般项目(LJ2020JCL015)。

摘　　要：针对单一分类器在森林分类中分类精度低的问题,提出一种改进的蚁群算法结合支持向量机的分类模型(ACOSVM)。该模型通过在蚁群搜索中加入部分有限搜索避免局部极值,并在信息素更新中引入时变函数,将动态更新策略与支持向量机结合,对径向基核函数参数进行优化。将该模型用于无人机可见光遥感影像森林类型分类,得到的实验结果为:在光谱特征影像分类中,对比ABC-SVM、GA-SVM、单纯的SVM模型,所提ACO-SVM在森林类型分类中效果最优,分类总体精度为81%,Kappa系数为0.7500;引入不同的纹理特征后,基于灰度共生矩阵特征对大兴安岭根河林区进行分类,总体分类精度为85%,Kappa系数为0.8063;引入Gabor纹理特征后,总体分类精度为87.5%,Kappa系数为0.8438。To solve the problem of a low classification accuracy of single classifier in forest classification, a classification model(ACO-SVM) that combines the improved ant colony algorithm(ACO) with support vector machine(SVM) is proposed. In the improved algorithm, a partial finite search was introduced into the ant colony search to avoid local extrema. A time-varying function was introduced into pheromone updating. The dynamic update policy was combined with SVM to optimize the parameters of the radial basis kernel function. The proposed model was verified using an experiment based on the classification of the forest types using UAV visible remote sensing images. In the spectral feature image classification, compared with ABC-SVM, GA-SVM, and conventional SVM models, the proposed ACO-SVM achieved the best forest-type classification performance, with an overall classification accuracy of 81% and a Kappa coefficient of 0. 7500. After introducing different textural features, the classification was performed for the Genhe forest area in the Greater Khingan Mountains based on the grayscale co-occurrence matrix feature, and the proposed ACO-SVM showed an overall classification accuracy of 85% and a Kappa coefficient of 0. 8063. After introducing the Gabor textural feature, ACO-SVM achieved the overall classification accuracy and Kappa coefficient of 87. 5% and 0. 8438, respectively.

关 键 词：森林分类 改进的蚁群算法 支持向量机 径向基核函数 

分 类 号：TP753[自动化与计算机技术—检测技术与自动化装置]