GEOMETRIC FEATURE SELECTION OF BUILDING SHAPE FOR URBAN CLASSIFICATION

Authors

  • Sergejs Kodors Rezekne academy of Technologies

DOI:

https://doi.org/10.17770/etr2017vol2.2613

Keywords:

feature selection, LiDAR, remote sensing, urban classification

Abstract

The proposed research is related with building detection in airborne laser scanning data. The result of geospatial surface segmentation provides a vector layer of unclassified shapes. Geometric features of shapes can be applied to classify urban objects and to detect buildings among them. The goal of this research is to select the appropriate geometric features considering their importance for building recognition. The feature selection is completed using random forest algorithm. The obtained list of features and their influence weights can be used to improve building recognition methods and to filter noise objects.

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Author Biography

  • Sergejs Kodors, Rezekne academy of Technologies
    SERGEJS KODORS is the researcher, who is working in Rezekne academy of Technologies. His main research field is the intersection of artificial intelligence and image processing with geospatial information systems and remote sensing. Contact him at sergejs.kodors@ru.lv

References

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Published

2017-06-15

How to Cite

[1]
S. Kodors, “GEOMETRIC FEATURE SELECTION OF BUILDING SHAPE FOR URBAN CLASSIFICATION”, ETR, vol. 2, pp. 78–83, Jun. 2017, doi: 10.17770/etr2017vol2.2613.