摘要
利用多时相的遥感数据制作的多维分类特征数据集,可以充分挖掘遥感影像中的植被信息提高地表覆被信息的分类精度。以世界三大盐碱土分布区之一的吉林省镇赉县为例,利用多时相Landsat8遥感数据制作的多维分类特征数据集,通过不同的分类方法提取了实验区11类地表覆被信息,并进行精度对比分析。结果表明:1支持向量机(SVM)法对苏打盐碱化土壤特殊生态环境的地表覆被信息提取具有较好的分类效果,总体分类精度87.77%,Kappa系数0.864 9;其中盐碱地的分类效果较好,生产精度达到98.34%。2不同方案分类精度从高到低依次为:支持向量机、最大似然分类、神经网络、最小距离、光谱角法。3镇赉县的土地利用类型以旱地、水田、盐碱地为主,镇赉西部以旱地为主要,中部地区盐碱地、碱泡、旱地交错分布,东部以水田为主。
Multidimensional classification feature data set based on multi-temporal remote sensing image can be fully mining the information to improve the land cover survey classification precision, multidimensional classification feature data set was used based on multi-temporal Landsat8 remote sensing image, through the different classifica-tion methods, extract the experimental area 11 kinds of land cover information and precision analysis in the Zhenlai County located in the one of the world’s three largest saline-alkali soil. The results show that ： ①the SVM has good effect on saline-alkali soil special ecological environment＇s land cover information extraction, overall accuracy 87. 77% , Kappa coefficient 0. 864 9; especial has good effect on saline-alkali soil, production accuracy 98. 34% , ② Different classification methodsr accuracy from high to low is support vector machine, maximum likelihood, neu-ral net, minimum distance, neural net;③ the most common use of land in the study area include dry land, paddy field, saline-alkali soil, the west to dry land as the main, the saline alkali land,alkali foam and dry land alterna-tive distribute in the central region, the east is given priority to with paddy field.
出处
《科学技术与工程》
北大核心
2017年第5期224-229,共6页
Science Technology and Engineering
基金
东北地区国土遥感综合调查
中国地质调查局项目(12120115063701)资助
关键词
多维分类特征数据集
支持向量机
半变异函数
盐碱区
多时相遥感
multidimensional classification feature data set SVM semi-variation function saline-al-kali soil multi-temporal remote
