西南林业大学学报2017，Vol.37Issue(4)：175-182,8.DOI:10.11929/j.issn.2095-1914.2017.04.025
基于Fisher判别的层次分类法的森林遥感影像分类
Fisher Discriminant Based Hierarchical Method for Forest Remotely Sensed Data Classification
摘要
Abstract
Fisher discriminant analysis has been applied to obtain the confusion matrix, which was used to cal-culate the classification evaluation index, and hierarchical classification tree model was built in accordance with these index. Thus, Fisher hierarchical classification method based on Fisher discriminant analysis was proposed to forest remote sensed images. A part of sample data from forest resources inventory and Landsat8 remote sensed image data of Hangzhou city were used to test this method, furthermore the results was compared to those of maximum likeli-hood classification method, support vector machine and random forest classification method. Results showed that the total classification accuracy of Fisher based hierarchical classification is 79. 45%, which is higher than that of maxi-mum likelihood classification, support vector machine and random forest by 12. 33%, 21. 00% and 10. 50%, respec-tively. Kappa coefficient was 0. 7568, which meant it was higher than those from maximum likelihood method, sup-port vector machine and random forest whose values were 0. 1455, 0. 2564 and 0. 1264, respectively. In the hierar-chical classification model based on Fisher discriminant analysis, the gradation of hierarchical classification tree are construction land→water→farmland→bamboo grove→broad-leaved forest→coniferous forest→conifer-broadleaf for-est. In the hierarchical classification based on Fisher discrimination, a model can only distinguish 1 category.关键词
Fisher判别法/层次分类法/影像分类/最大似然/支持向量机/随机森林Key words
Fisher discriminant analysis/hierarchical classification/image classification/maximum likeli-hood/SVM/random forest分类
农业科技引用本文复制引用
杜靖媛,葛宏立,路伟,孟森..基于Fisher判别的层次分类法的森林遥感影像分类[J].西南林业大学学报,2017,37(4):175-182,8.基金项目
国家自然科学基金项目 (41371411) 资助. （41371411）
