城市植被尺度鉴别与分类研究

城市地物具有多尺度分布特点,尺度鉴别与确定是分类的前提。提出改进的面积相对差指标,根据城市植被的分布状态确定最优分割尺度。采用面向对象方法,利用对象的光谱和空间信息对高空间分辨率影像进行植被分类。与基于像元的传统光谱分类方法和单尺度分类结果比较,最优分割尺度的鉴别和面向对象的分类方法分类精度较高,6种城市植被的分类总精度达85.5%,Kappa系数为0.83;同时有效抑制了光谱数据分类中存在的地物破碎问题。

Scale Identification for Urban Vegetation Classification Using High Spatial Resolution Satellite Data

The scale identification is an important issue for the vegetation classification in the same urban landscape.In this paper,a method of scale identification and the determination criterion for urban vegetation image segmentation using high spatial resolution remotely sensed imagery are proposed.The criterion of the relevant deviation with two parameters,area and number of object,is used to optimize the scale of urban objects.The effect of the optimizing scales is examined.A hierarchy classification is performed for six vegetation types using the fuzzy k-means classifier.The results show that overall accuracy is 85.5% for the proposed approach,and 69.7% and 65.5% for k-mean classifier with single scale and MLC(Maximum Likelihood Classifier),respectively.The improvement is achieved by the proposed method of determination scale,in which the criterion and the multi-scales classification for urban vegetation types are of the most critical values.