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Extraction of buildings from remote sensing imagery based on multi-scale SLIC-GMRF and FCNSVM

  • role： First author第一作者
  • Affiliation：

    College of Resources Environment & Tourism, Capital Normal University, Beijing 100048, China

    Key Laboratory of 3D Information Acquisition and Application of Ministry, Beijing 100048, China

    Key Laboratory of Resources Environment and GIS of Beijing Municipal，Beijing 100048，China

  • Email：15911157479@163.com
  • Introduction：井然，1991年生，男，博士研究生，研究方向为遥感技术及地学应用。E-mail: 15911157479@163.com
JING Ran123,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Resources Environment & Tourism, Capital Normal University, Beijing 100048, China

    Key Laboratory of 3D Information Acquisition and Application of Ministry, Beijing 100048, China

    Key Laboratory of Resources Environment and GIS of Beijing Municipal，Beijing 100048，China

  • Email：gongzhn@163.com
  • Introduction：宫兆宁，1976年生，女，副教授，研究方向为遥感技术及地学分析应用。E-mail：gongzhn@163.com
GONG Zhaoning123*,  
  • Affiliation：

    College of Resources Environment & Tourism, Capital Normal University, Beijing 100048, China

    Key Laboratory of 3D Information Acquisition and Application of Ministry, Beijing 100048, China

    Key Laboratory of Resources Environment and GIS of Beijing Municipal，Beijing 100048，China

ZHU Wending123,  
  • Affiliation：

    College of Resources Environment & Tourism, Capital Normal University, Beijing 100048, China

    Key Laboratory of 3D Information Acquisition and Application of Ministry, Beijing 100048, China

    Key Laboratory of Resources Environment and GIS of Beijing Municipal，Beijing 100048，China

GUAN Hongliang123,  
  • Affiliation：

    College of Resources Environment & Tourism, Capital Normal University, Beijing 100048, China

    Key Laboratory of 3D Information Acquisition and Application of Ministry, Beijing 100048, China

    Key Laboratory of Resources Environment and GIS of Beijing Municipal，Beijing 100048，China

ZHAO Wenji123,  
  • Affiliation：

    Satellite Surveying and Mapping Application Center , State Bureau of Surveying and Mapping , Beijing 100048, China

ZHANG Tao4

Resümee

The extraction of buildings from remote sensing imagery has an important application value. However, high-resolution images contain detailed information and complex features that hinder the difficulty of building extraction process.To address this problem, we propose a building extraction method of building extraction based on multi-scale SLIC-GMRF and FCNSVM that demonstrates an improved ability of extracting buildings from high-resolution remote sensing images to some extent. First, a multi-scale SLIC-GMRF segmentation algorithm is applied to determine the initial building area, and then the advantages of the FCN neural network in semantic segmentation are utilized to extract the building features. Second, the extracted building features are used to train an SVM classifier to refine the building extraction results of building.The results of three control experiments and two comparative tests reveal that the SLIC segmentation algorithm affects the initial segmentation results, the SVM classifier affects the extraction of building details, and the FCN features influence the performance of the SVM classifier. The precision rate, recall rate, and quality index of the proposed method are all better than the compared methods.The following conclusions can be drawn from the experimental results. For the study area with clear features and minimal obstructions, the proposed method can effectively extract buildings from an image. This method can also obtain ideal results for areas with a complex distribution of buildings can also get ideal results.

Schlüsselwort

remote sensing;building extraction;image segmentation;FCN neural network;SVM;high-resolution remote sensing image

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