Object-based Detection and Classification of Vehicles from High-resolution Aerial Photography

Vehicle counts and truck percentages are important input variables in both noise pollution and air quality models, but the acquisition of these variables through fixed-point methods can be expensive, labor-intensive, and provide incomplete spatial sampling. The increasing availability and decreasing cost of high spatial resolution imagery provides an opportunity to improve the descriptive ability of traffic volume analysis. This study describes an object-based classification technique to extract vehicle volumes and vehicle type distributions from aerial photos sampled throughout a large metropolitan area. The authors developed rules for optimizing segmentation parameters, and used feature space optimization to choose classification attributes and develop fuzzy-set memberships for classification. Vehicles were extracted from street areas with 91.8% accuracy. Furthermore, separation of vehicles into classes based on car, medium-sized truck, and buses/heavy truck definitions was achieved with 87.5% accuracy. Implications of these results for traffic volume analysis and parameterization of existing noise and air pollution models are discussed, and future work for traffic assessment using high-resolution remotely-sensed imagery is suggested.

  • Availability:
  • Authors:
    • Holt, Ashley C
    • Seto, Edmund Y W
    • Rivard, Tom
    • Gong, Peng
  • Publication Date: 2009-7

Language

  • English

Media Info

  • Media Type: Print
  • Features: Figures; Photos; References; Tables;
  • Pagination: pp 871-880
  • Serial:

Subject/Index Terms

Filing Info

  • Accession Number: 01135558
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Jul 21 2009 8:13AM