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当前位置: 首页 > 文章 > 面向对象的GF-1遥感影像多尺度分割研究 甘肃农业大学学报 2018 (4) 116-123
Position: Home > Articles > Study on multi-scale segmentation of GF-1 remote sensing image with object-oriented method Journal of Gansu Agricultural University 2018 (4) 116-123

面向对象的GF-1遥感影像多尺度分割研究

作  者：
张华;张改改
单  位：
兰州大学生命科学学院;西北师范大学地理与环境科学学院
关键词：
面向对象;影像分割;最优分割尺度;尺度评价;RMAS
摘  要：
【目的】探索基于GF-1遥感影像的干旱区绿洲地物的最优分割尺度,为民勤绿洲地物的高精度提取奠定基础.【方法】本研究采用了综合类内一致性与类间异质性两种指数构建的RMAS分割指数,以国产GF-1遥感影像为数据源,民勤绿洲为试验区,获取了建筑物、林地与耕地的最优分割尺度;通过参考对象与分割对象之间的光谱与位置关系,提出了使用光谱相似性指数SSI以及分割距离指数SDI对分割质量进行评价;使用最邻近分类法对试验区地物进行提取.【结果】1)试验区建筑物、林地以及耕地的最优分割尺度为50、30、80;2)最优分割尺度下,SSI指数接近0,且SDI指数最小;3)地物提取的总体精度为87.50%,Kappa系数为84.35%,3种地物在最优分割尺度下提取的精度分别为82.86%、89.29%、96.15%.【结论】通过RMAS指数能够准确获取最优分割尺度,以达到较高的地物提取精度.
译  名：
Study on multi-scale segmentation of GF-1 remote sensing image with object-oriented method
作  者：
ZHANG Hua;ZHANG Gai-gai;College of Geography and Environment Science,Northwest Normal University;College of Life Sciences,Lanzhou University;
关键词：
object-oriented;;image segmentation;;optimal scale;;scale evaluation;;RMAS
摘  要：
【Objective】To explore the optimal segmentation scale of the arid region based on GF-1 remote sensing image,and lay the foundation for the high accuracy extraction of the ground objects of Minqin oasis.【Method】The RMASsegmentation index was constructed by combining interclass consistency and interclass heterogeneity,which was used to obtain the optimal segmentation scale of the building,woodland and cultivated land.GF-1 remote sensing image was used as the data source.Considering the relationship between spectral and position,the spectral similarity index(SSI)and the segmentation distance index(SDI)were used to evaluate the segmentation result.The nearest neighbor classification was used to extract the features of the research area.【Result】The optimal scale was 50,30 and 80 respectively.In the optimal segmentation scale,the SSIindex was close to 0 and the SDIindex was the smallest.The overall classification accuracy was 87.50%,and the kappacoefficient was 84.35%.The producer accuracy of the building,woodland and cultivated land were 82.86%,89.29% and 96.15% respectively.【Conclusion】The optimal segmentation scale could be accurately obtained by RMASindex for high extraction accuracy.

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