以QuickBird高分辨率遥感影像为主要数据源,采用多尺度影像分割方法提取地物对象的光谱、纹理和形状特征; 在此基础上,构建基于随机森林(RF)方法的遥感影像分类模型,分析和评价特征变量对模型重要性与稳定性的影响。结果表明:① 研究区最优分割尺度参数为70、形状因子0.2、色彩因子0.8,同时构建研究区乔木、灌木和草地等8个景观类型的光谱、纹理和形状等32个特征变量信息; ② 选择5 000棵树和1个节点变量构建的RF分类模型的总体精度为0.94,Kappa系数为0.93,OOB(Out of Bag)数据泛化误差为6.01%; ③ 通过分析特征变量的重要性发现,Ratio_la_1和Ratio_la_2等光谱特征的重要性值明显比形状特征和纹理特征的高; ④ 基于平均下降精度,选择16个变量构建RF模型时总体精度达到0.94,Kappa系数0.93; ⑤ 基于基尼指数构建的RF模型,在19个变量时总体精度和Kappa系数达到峰值。相比较而言,基于平均下降精度构建的RF较稳定。
Abstract
With the support of multi-scale segmentation of object-oriented classification method, features and characteristics variables were established and extracted by taking the QuickBird high-resolution remote sensing image as the main data source. High resolution remote sensing image classification model was built based on RF(Random Forest), the importance and sensitivity of characteristic variables to the model were analyzed. The research results showed that the optimal segmentation parameter in the study area was determined: segmentation scale parameter is 70, shape factor is 0.2, color factor is 0.8,and 32 characteristics such as spectrum, texture and shape variable information of eight landscape types were extracted; the overall accuracy of RF classification model which was built by choosing 5 000 trees and one node variable is 0.94, the Kappa coefficient was 0.93, OOB(Out of the Bag)data generalization error is 6.01%; through the analysis of the importance of variables, the importance value of spectral characteristics was obviously higher than that of shape features and texture features; based on mean decrease accuracy the overall accuracy is 0.94, the Kappa coefficient was 0.93 when 16 variables were used to built RF model; but they achieved the peak when 19 variables were used to built the model based on mean decrease Gini coefficient, the sensitivity analysis of the model which based on mean decrease accuracy was optimum than which based on mean decrease Gini coefficient. The feasibility of random forest model classification method should be further explored to provide scientific and rational reference for the high resolution remote sensing image classification.
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基金
收稿日期:2013-12-13 修回日期:2014-05-19
基金项目:国家自然科学基金项目(30972415); 江苏高校优势学科建设工程资助项目(PAPD)
第一作者:刘海娟,硕士生; 张婷,硕士生。*通信作者:徐雁南,教授。E-mail: nfuxyn@126.com。
引文格式:刘海娟,张婷,侍昊,等. 基于RF模型的高分辨率遥感影像分类评价[J]. 南京林业大学学报:自然科学版,2015,39(1):99-103.
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