基于面向对象随机森林分类模型的滑坡遥感解译
|
摘要
滑坡遥感解译可为复杂艰险山区的道路选线提供重要的参考资料。以青海省沿黄公路共和至大河家段公路为研究区,利用高分辨率QuickBird卫星影像,采用面向对象的随机森林分类模型提取滑坡区域。该方法先利用多尺度分割方法生成滑坡对象,构建滑坡对象的光谱、纹理及形状特征图,在此基础上利用训练好的随机森林模型提取出疑似滑坡区,最后结合目视解译,得到准确的滑坡区。结果表明:该方法能快速准确地提取出道路沿线的滑坡区,有效节约人力物力,为后续的道路选线提供准确信息。
引文
[1]黄润秋.20世纪以来中国的大型滑坡及其发生机制[J].岩石力学与工程学报,2007(3).
[2]王治华.数字滑坡技术及其在天台乡滑坡调查中的应用[J].岩土工程学报,2006(4).
[3]Guzzetti F,Mondini A C,Cardinali M,et al.Landslide Inventory Maps:New Tools for an Old Problem[J].EarthScience Reviews,2012,112(1-2):42-66.
[4]李丽,华晓宾,王玉娟.遥感技术在公路建设项目综合环境长期监测及评估中的应用[J].中外公路,2012(1).
[5]余绍淮,钱昌静,张霄.基于遥感技术的公路地质灾害危险性评估方法[J].中外公路,2016(4).
[6]童立强,郭兆成.典型滑坡遥感影像特征研究[J].国土资源遥感,2013(1).
[7]Murillo-García F G,Alcántara-Ayala I,Ardizzone F,et al.Erratum to:Satellite Stereoscopic Pair Images of very High Resolution:A Step Forward for the Development of Landslide Inventories[J].Landslides,2015,12(2):293-293.
[8]姚鑫,张永双,王献礼,等.基于地貌特征的浅层崩滑体遥感自动识别[J].地质通报,2008(11).
[9]Barlow,J.,Franklin,S.,Martin,Y.High Spatial Resolution Satellite Imagery,Dem Derivatives,And Image Segmentation for the Detection of Mass Wasting Processes[J].Photogrammetric Engineering and Remote Sensing,2006,(72):687-692.
[10]Martha T R,Kerle N,Jetten V,et al.Characterising Spectral,Spatial and Morphometric Properties of Landslides for Semi-Automatic Detection Using Object-O-riented Methods[J].Geomorphology,2010,116(1-2):24-36.
[11]宿方睿,郭长宝,张学科,等.基于面向对象分类法的川藏铁路沿线大型滑坡遥感解译[J].现代地质,2017(5).
[12]Breiman L.Random Forests[J].Machine Learning,2001,45(1):5-32.
[2]王治华.数字滑坡技术及其在天台乡滑坡调查中的应用[J].岩土工程学报,2006(4).
[3]Guzzetti F,Mondini A C,Cardinali M,et al.Landslide Inventory Maps:New Tools for an Old Problem[J].EarthScience Reviews,2012,112(1-2):42-66.
[4]李丽,华晓宾,王玉娟.遥感技术在公路建设项目综合环境长期监测及评估中的应用[J].中外公路,2012(1).
[5]余绍淮,钱昌静,张霄.基于遥感技术的公路地质灾害危险性评估方法[J].中外公路,2016(4).
[6]童立强,郭兆成.典型滑坡遥感影像特征研究[J].国土资源遥感,2013(1).
[7]Murillo-García F G,Alcántara-Ayala I,Ardizzone F,et al.Erratum to:Satellite Stereoscopic Pair Images of very High Resolution:A Step Forward for the Development of Landslide Inventories[J].Landslides,2015,12(2):293-293.
[8]姚鑫,张永双,王献礼,等.基于地貌特征的浅层崩滑体遥感自动识别[J].地质通报,2008(11).
[9]Barlow,J.,Franklin,S.,Martin,Y.High Spatial Resolution Satellite Imagery,Dem Derivatives,And Image Segmentation for the Detection of Mass Wasting Processes[J].Photogrammetric Engineering and Remote Sensing,2006,(72):687-692.
[10]Martha T R,Kerle N,Jetten V,et al.Characterising Spectral,Spatial and Morphometric Properties of Landslides for Semi-Automatic Detection Using Object-O-riented Methods[J].Geomorphology,2010,116(1-2):24-36.
[11]宿方睿,郭长宝,张学科,等.基于面向对象分类法的川藏铁路沿线大型滑坡遥感解译[J].现代地质,2017(5).
[12]Breiman L.Random Forests[J].Machine Learning,2001,45(1):5-32.