基于迭代CART算法分层分类的土地覆盖遥感分类
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摘要
土地覆盖遥感分类是土地利用变化监测及其空间格局分析的重要技术手段。为了进一步提高土地覆盖遥感分类精度,提出一种基于迭代CART算法分层分类的新技术体系。首先,根据类别光谱可分性分析,确定分层分类方法的类别提取顺序依次为水体、植被、裸地和建设用地。然后,在此分类顺序下,分别确定各类别的图像分割参数和分类特征集。最后,在对象尺度上,将CART算法迭代引入分层分类过程,不断选取训练样本进行CART算法的迭代分类依次提取前3个类别,将剩余部分直接划分为建设用地。实验结果证明:该方法可以明显减轻裸地和建设用地的混分现象,获得较高精度的土地覆盖分类结果,总体精度85.76%,Kappa系数0.72。相比于SVM、CART两种单次分类方法,总体精度和Kappa系数分别提升了10.67%～16.5%和0.15～0.21。同时,该方法能够灵活调整某个类别的分类精度并具有很强的扩展性,可以为其他涉及图像分类的遥感应用领域提供方法参考。
Land cover classification based on remote sensing is an important means to analyze the change and spatial pattern of land use.In order to further improve the classification accuracy,this paper proposed a hierarchical classification and iterative CART model based method for remote sensing classification of landcover.Firstly,the extraction order of land cover classes was determined based on the class separability evaluation,which was water,vegetation,bare soil and built-up land.Secondly,we selected the optimal image segmentation parameters and a set of sensitive features for each class during the hierarchical classification process.Finally,object-based training samples were selected to be fed into the iterative CART algorithm for the successive extraction of the first three classes,with the remaining unclassified objects being directly assigned to the last class.Results demonstrated that the proposed method can significantly reduce the mixture between bare soil and built-up land,and is capable of achieving landcover classification with much higher accuracy.The proposed method achieved an overall accuracy of 85.76% and a Kappa efficient of 0.72,with the performance improvements ranging from 10.67% to 16.5% and 0.15 to 0.21 as compared SVM and CART single classification methods.The classification accuracy of a specific class can be flexibly adjusted using this method,giving different purposes of classification.This method can also be easily extended to other districts and disciplines involving remote sensing image classification.
Land cover classification based on remote sensing is an important means to analyze the change and spatial pattern of land use.In order to further improve the classification accuracy,this paper proposed a hierarchical classification and iterative CART model based method for remote sensing classification of landcover.Firstly,the extraction order of land cover classes was determined based on the class separability evaluation,which was water,vegetation,bare soil and built-up land.Secondly,we selected the optimal image segmentation parameters and a set of sensitive features for each class during the hierarchical classification process.Finally,object-based training samples were selected to be fed into the iterative CART algorithm for the successive extraction of the first three classes,with the remaining unclassified objects being directly assigned to the last class.Results demonstrated that the proposed method can significantly reduce the mixture between bare soil and built-up land,and is capable of achieving landcover classification with much higher accuracy.The proposed method achieved an overall accuracy of 85.76% and a Kappa efficient of 0.72,with the performance improvements ranging from 10.67% to 16.5% and 0.15 to 0.21 as compared SVM and CART single classification methods.The classification accuracy of a specific class can be flexibly adjusted using this method,giving different purposes of classification.This method can also be easily extended to other districts and disciplines involving remote sensing image classification.
引文
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[9] Huang C,Davis L S,Townshend J R G.An Assessment of Support Vector Machines for Land Cover Classification[J].International Journal of Remote Sensing,2002,23(4):725-749.
[10] Ji Min,Li Hui,Shi Xiaochun.Classification of Urban Land Use based on Object-oriented Method[J].Geospatial Information,2009,7(3):62-65.[纪敏,李辉,石晓春.面向对象的城市土地利用分类[J].地理空间信息,2009,7(3):62-65.]
[11] Liu Liya.Based on China-Made GF1 for Land Use/Cover Classification of High Cold Mountain Areas[D].Hangzhou:Zhejiang University,2016.[刘丽雅.基于国产GF-1的高寒山区土地利用/覆盖分类研究[D].杭州:浙江大学,2016.]
[12] Scott G J,Marcum R A,Davis C H,et al.Fusion of Deep Convolutional Neural Networks for Land Cover Classification of High-resolution Imagery[J].IEEE Geoscience and Remote Sensing Letters,2017,14(9):1638-1642.
[13] Chen Wenjiao,Weng Yongling,Lu Yunge.Extraction of Land Use and Land Cover Information based on Multilevel Decision Tree Classification[J].Geomatics & Spatial Information Technology,2017,40(9):63-68.[陈文娇,翁永玲,路云阁.基于多级决策树分类的土地利用与覆盖信息提取[J].测绘与空间地理信息,2017,40(9):63-68.]
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