北京大学学报（自然科学版）2018，Vol.54Issue(1)：105-114,10.DOI:10.13209/j.0479-8023.2017.073
基于形态学属性剖面和单类随机森林分类的道路路域新增建筑物提取方法
A Method for Extraction of Newly-Built Buildings in Road Region Using Morphological Attribute Profiles and One-Class Random Forest
摘要
Abstract
The authors present a method for extraction of newly-built buildings in road-region using morpho-logical attribute profiles and one-class random forest. The morphological attribute profiles are first obtained from bitemporal high-resolution remote sensing images. The morphological attribute profiles obtained and spectral features are then combined to extract newly-built buildings along road-regions using an improved one-class random forest. Bitemporal images of the Daoxiang Lake area in Beijing are used as experimental data to validate the proposed method, by quantitatively comparing with two conventional change detection methods, i.e., direct bitemporal classification and post-classification comparison methods based on support vector machine. The experimental results show that the accuracy of newly-built building extraction from the proposed method (i.e. using combined spectral features and attribute profiles) is significantly higher than that using only the spectral features, with an increase of 15.11% in Kappa. In addition, the Kappa of the proposed method is 1.78% and 25.15% higher than that of the direct bitemporal classification and that of the post-classification comparison. Therefore, the experimental results validate the effectiveness of the proposed method. Advantages of the one-class random forest include capabilities to effectively deal with high-dimensional data and measure the importance of different features used in one-class classification.关键词
高分辨率遥感影像/道路路域/建筑物变化检测/形态学属性剖面/单类随机森林Key words
high-resolution remote sensing image/road-region/building change detection/morphological attribute profiles/one-class random forest分类
天文与地球科学引用本文复制引用
史忠奎,李培军,罗伦,阳柯..基于形态学属性剖面和单类随机森林分类的道路路域新增建筑物提取方法[J].北京大学学报（自然科学版）,2018,54(1):105-114,10.基金项目
中国交通通信信息中心公路交通领域军民融合应用示范项目(GFZX0404080102)资助 （GFZX0404080102）
