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.
This work was supported by the Austrian Science Fund Projects W1209 and P18600 under the doctoral program Confluence of Vision and Graphics, by the FFG projects APAFA (813397) and AUTOVISTA (813395), financed by the Austrian Research Promotion Agency, and by the Austrian Joint Research Project Cognitive Vision under the projects S9103-N04 and S9104-N04.
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Kluckner, S., Mauthner, T., Roth, P.M., Bischof, H. (2010). Semantic Classification in Aerial Imagery by Integrating Appearance and Height Information. In: Zha, H., Taniguchi, Ri., Maybank, S. (eds) Computer Vision – ACCV 2009. ACCV 2009. Lecture Notes in Computer Science, vol 5995. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12304-7_45
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DOI: https://doi.org/10.1007/978-3-642-12304-7_45
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