GF-2影像面向对象典型城区地物提取方法

Object-oriented Extraction Method of Typical Urban Features Based on GF-2 Images

国产高分遥感影像信息丰富,提供了精准的地物空间细节,深入研究高分数据处理及其提取城区地类目标信息的方法具有重要意义。本文以国产高分二号(GF-2)遥感影像为数据源,利用规则集的面向对象分类方法,通过ESP尺度分析工具选取得出最优分割尺度,建立各类地物的特征体系及分类规则,最终提取出研究区典型城区地物信息,并将之与传统基于像元的SVM监督分类结果作比较。结果表明:规则集的面向对象分类总体精度为92.23%,Kappa系数为0.9,比SVM监督分类有大幅度提高。对高分二号等高分辨率影像,面向对象的分类方法精度更高,图示效果更好,是城区地物提取的有效方法。

Domestic Gaofcn remote sensing images have abundant inforruation,which can provide accurate details of spatial objects.It is of great significance to study Gaofen data processing and the method of extracting urban objects in depth. Taking the remote sensing image of domestic GF-2 as the data source,the optimal segmentation scale is obtained through the ESP scale analysis tool which is based on the object-oriented classification method of rule set.And then the t^ature system and the classification rules of various features are established to extract the information of typical urban features, Tbe results are compared with the traditional pixel-based SVM supervised classification.The results show that the overall accuracy of object-oriented classification of rule set is 92.23%,and the Kappa coefficient is 0.9,which is significantly improved compared with SVM supervised classification.For high-resolution images such as GF- 2,the object-oriented classification method is more accurate and has a better graphical effect, which is an effective method for urban object extraction.