Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography (한국측량학회지)

Korean Society of Surveying, Geodesy, Photogrammetry and Cartography (한국측량학회)
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Automatic Classification of Drone Images Using Deep Learning and SVM with Multiple Grid Sizes

  • Kim, Sun Woong (Department of Advanced Technology Fusion, Konkuk University) ;
  • Kang, Min Soo (Jigusoft Inc) ;
  • Song, Junyoung (Department of Civil and Environmental Engineering, Konkuk University) ;
  • Park, Wan Yong (Agency for Defense Development) ;
  • Eo, Yang Dam (Department of Civil and Environmental Engineering, Konkuk University) ;
  • Pyeon, Mu Wook (Department of Civil and Environmental Engineering, Konkuk University)
  • Received : 2020.08.29
  • Accepted : 2020.10.13
  • Published : 2020.10.31
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Abstract

SVM (Support vector machine) analysis was performed after applying a deep learning technique based on an Inception-based model (GoogLeNet). The accuracy of automatic image classification was analyzed using an SVM with multiple virtual grid sizes. Six classes were selected from a standard land cover map. Cars were added as a separate item to increase the classification accuracy of roads. The virtual grid size was 2-5 m for natural areas, 5-10 m for traffic areas, and 10-15 m for building areas, based on the size of items and the resolution of input images. The results demonstrate that automatic classification accuracy can be increased by adopting an integrated approach that utilizes weighted virtual grid sizes for different classes.

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References

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