Semantic classification in aerial imagery by integrating appearance and height information

  • Authors:

  • Stefan Kluckner;Thomas Mauthner;Peter M. Roth;Horst Bischof

  • Affiliations:

  • Institute for Computer Graphics and Vision, Graz University of Technology, Austria;Institute for Computer Graphics and Vision, Graz University of Technology, Austria;Institute for Computer Graphics and Vision, Graz University of Technology, Austria;Institute for Computer Graphics and Vision, Graz University of Technology, Austria

  • Venue:
  • ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
  • Year:
  • 2009

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Abstract

In this paper we present an efficient technique to obtain accurate semantic classification on the pixel level capable of integrating various modalities, such as color, edge responses, and height information. We propose a novel feature representation based on Sigma Points computations that enables a simple application of powerful covariance descriptors to a multi-class randomized forest framework. Additionally, we include semantic contextual knowledge using a conditional random field formulation. In order to achieve a fair comparison to state-of-the-art methods our approach is first evaluated on the MSRC image collection and is then demonstrated on three challenging aerial image datasets Dallas, Graz, and San Francisco. We obtain a full semantic classification on single aerial images within two minutes. Moreover, the computation time on large scale imagery including hundreds of images is investigated.