摘要
针对城市水体与建筑物阴影、沥青路面和浓密植被等暗地物的光谱混淆性,构建了结合光谱特征和空间特征的城市水体提取知识决策树。其基本思路为:首先利用短波红外波段提取暗地物,其次分别利用浓密植被在近红外波段和沥青路面在红波段中的反射率剔除这两类暗地物,再次利用空间密度特征剔除建筑物阴影,最后根据面积对水体进行补充识别。与现有方法相比,本方法提出了城市水体提取中需关注的暗地物类型并开展针对性特征分析,并利用由噪声环境下密度聚类方法(DBSCAN)描述的空间密度特征区分城市水体和建筑物阴影。对北京城区SPOT 5多光谱影像开展的实验得到的检测率为86.18%,虚警率为13.82%,表明本方法是基于中分辨率多光谱影像提取城市水体的有效方法。
In view of the spectral mixing between water body,building shadow,asphalt road and dense vegetation in urban environment,a knowledge-based decision tree combining spectral and spatial features is constructed to extract water body thematic information in this paper.Firstly,dark objects in urban environment are extracted using threshold of reflectance in SWIR.Secondly,dense vegetation and asphalt road are eliminated according to their reflectance in NIR and R respectively.Thirdly,differences in spatial density are used to eliminate building shadow.Finally,area threshold is used for supplementary recognition of water body.The consideration of dark objects in urban water body extraction,and the using of spatial density described by DBSCAN in discriminating water body from building shadow are two main differences between the proposed decision tree and state-of-art methods.SPOT-5 multispectral imagery of Beijing is used to validate the proposed knowledge-based decision tree.The detection rate is 86.18% and false alarm rate is 13.82%.It can be concluded that the proposed model is an effective method in water body thematic information extraction based on medium-resolution multi-spectral imagery in urban environment.
出处
《遥感信息》
CSCD
2013年第1期29-33,37,共6页
Remote Sensing Information
基金
国土资源高分辨率对地观测系统应用示范系统先期攻关项目(E0202/1112)
高分辨率信息产品"生产线"系统先期攻关项目(E0104/1112)
