欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
基于信息相关理论,根据相关度值动态调整分类器的组合和权重,建立了新型的多分类器集成规则,并应用于决策树分类器、BP人工神经网络分类器和SVM分类器的集成。通过对长沙城区TM影像的分类实验发现：①三种分类器的分类结果存在较明显的差异,水体区的差异像元最少,占水体总像元的15.12%,建设用地区的差异像元最多,占建设用地区像元的54.93%;②三种分类器均具有较高的分类精度,总体精度均超过了74%,而且分类器各有优势,决策树分类器能够较好地分出水体和建设用地,BP分类器能够较好地分出水体和林地,SVM分类器对水体、林地和建设用地均有较高的分类精度;③基于全信息相关度的多分类器集成分类法明显地提升了分类结果的精度,分类精度达到了85.71%,Kappa系数达到了80.56%。
英文摘要：
A new multiple classifiers combination algorithm based on the theories of information relations is proposed,it can dynamically adjust the weights of different classifiers.The new algorithm is used to combine decision tree algorithm,BP,and SVM.The experimental results of TM image of Changsha city in China show that：① There are significant difference among the results of three algorithms,the proportion of pixels with different classes in water area is 15.12%,and the proportion of pixels with different classes in building area is 54.93%;② Three algorithms get high classification precision and every classifier has different advantages,decision tree algorithm can distinguish the water area,and building area,BP algorithm can distinguish water area,and wood area and SVM algorithm can distinguish water area,wood area and building area;③ The algorithm proposed in this paper has the highest precision,the total precision is 85.71%,and the Kappa coefficient is 80.56%.
同期刊论文项目
同项目期刊论文
Study on the urban heat island effects and its relationship with surface biophysical characteristics
Simulation multi-objective spatial optimization allocation of land use based on the integration of m
期刊信息
位置：