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

doi:10.13474/j.cnki.11-2246.2018.0027

Abstract

Abstract:

Domestic Gaofen remote sensing images have abundant information,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 feature system and the classification rules of various features are established to extract the information of typical urban features. The 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.

Key words: GF-2, object-oriented, multi-scale segmentation, typical urban feature

CLC Number:

P237

WANG Lei, YANG Wunian, REN Jintong, DENG Xiaoyu. Object-oriented Extraction Method of Typical Urban Features Based on GF-2 Images[J]. 测绘通报, 2018, 0(1): 138-142.

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