摘要
遥感图像分类是从复杂的地物类型中提取有效类别信息的过程。在研究区地物类型较为复杂的情况下,借助计算机对遥感数据进行类别预测可以提高分类效率和准确率。本文基于Weka平台,利用决策树C4.5算法构造分类模型,进而对未知类别数据进行预测。实验分析表明,基于Weka平台利用决策树C4.5算法对遥感图像分类是可行且有效的,且分类精度较高。
Remote sensing image classification is a process of extracting effective class information from complex object types. The classification efficiency and accuracy can be improved by using the computer in the complex study area. In this paper, based on the Weka platform, using the decision tree CA. 5 algorithm to construct the classification model, and then to predict the unknown class data. The experimental results show that the decision tree algorithm based on Weka platform is feasible and effective for remote sensing image classification, and the classification accuracy is higher.
出处
《测绘与空间地理信息》
2017年第5期52-55,共4页
Geomatics & Spatial Information Technology
基金
航空遥感技术国家测绘地理信息局重点实验室经费资助课题(2014B09)
吉林省省级大学生创新创业训练计划项目(201611437107)
吉林省教育厅"十二五"科学技术研究项目(吉教科合字[2015]第298号)资助
关键词
WEKA平台
遥感图像分类
决策树算法
离散化
Weka platform
remote sensing image classification
decision tree algorithm
discretization
