OBIA与RF结合的龙口市土地利用信息提取方法

The extraction approach of land use information combining OBIA with RF in Longkou city

为提高中分辨率遥感影像解译精度,本文提出面向对象影像分析(Object Based Image Analysis,OBIA)与随机森林(Random Forest,RF)结合的土地利用信息提取方法。采用Landsat8 OLI影像,针对不同地物特点,阈值分割和多尺度分割结合创建影像对象,规则集和分类器协同分类,基于Relief F算法分别对光谱特征、纹理特征及所有特征降维筛选特征子集,并与全部特征一起应用RF建模,对龙口市进行土地利用信息提取与比较。结果表明:OBIA与RF结合提取土地利用信息,基于Relief F算法筛选纹理特征,保留完整光谱、几何、空间关系特征构建RF模型,建模错分率为0.0958,分类总体精度和Kappa系数分别为89.37%和0.872,取得较理想结果。该方法可应用于中分辨率遥感影像土地利用信息提取。

In order to improve the interpretation precision of the medium resolution satellite image,this paper proposed a new extraction approach of land use information combining Object Based Image Analysis(OBIA)with Random Forest(RF).Using the Landsat 8 OLI image and according to the features of all kinds of ground objects,the image objects were created combined with the multi-threshold and multi-resolution segmentation method,and the rule set and classifier were collaboratively used in the image classification.The Relief F algorithm was used to dimensionally reduce the spectral,texture and all feature variables,and to select 3 feature subsets.Then the RF model was conducted with the 3 feature subsets and all feature subset to build 4 models.The 4 models were applied to extract land use information in Longkou city,and the results were compared.The result indicated that the OOB(Out of Bag)misclassification,classification accuracy and Kappa index were 0.0958,89.37%and 0.872 respectively with the land use information extraction approach combining OBIA with RF,dimension reduction based on the Relief F algorithm only for texture features.This retained the complete spectral,geometric and spatial features,which has a higher accuracy.The approach can be applied to the extraction of land use information with the medium resolution satellite image.