OBIA与RF结合的龙口市土地利用信息提取方法

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Landsat 8 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。

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

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.