测绘通报 ›› 2018, Vol. 0 ›› Issue (2): 72-77.doi: 10.13474/j.cnki.11-2246.2018.0047

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Multi-feature Multiple Kernels SVM-based Urban Road Extraction

LI Hongchuan1, CHU Heng1,2,3, HUO Yinghai1   

  1. 1. School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
    2. School of Geographical Sciences, Southwest University, Chongqing 400715, China;
    3. Chongqing Survey Institute, Chongqing 400020, China
  • Received:2017-05-23 Revised:2017-11-02 Online:2018-02-25 Published:2018-03-06

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Abstract:

For the complex nature of urban road extraction in high-resolution remote sensing image and the classification performance of SVM,a new urban road extraction method of multi-feature multiple kernels SVM-based is proposed.Firstly,the FCM algorithm is utilized to classify the original image into the built-up areas and non-built-up areas,and the non-built-up areas are removed.Then this paper segments the built-up areas using watershed segmentation algorithm,and extracts the image objects spectral features and spatial features.This paper uses the weighted sum approach to achieve multiple kernels SVM (MKSVM) by the way of combining the global kernel function and the local kernel function.The secondary classification of built-up areas are achieved using MKSVM and remove the non-road information.Finally,the final road information is processed by using mathematics morphology.The result of experiment shows that the proposed method can fairly well extract the urban road information,and the accuracy of classification is much higher than SVM and other extraction methods.

Key words: high-resolution remote sensing imagery, urban road extraction, multiple kernels SVM, secondary classification, mathematics morphology

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