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Looking Outside the Box: The Role of Context in Random Forest Based Semantic Segmentation of PolSAR Images

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Pattern Recognition (DAGM GCPR 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12544))

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Abstract

Context - i.e. information not contained in a particular measurement but in its spatial proximity - plays a vital role in the analysis of images in general and in the semantic segmentation of Polarimetric Synthetic Aperture Radar (PolSAR) images in particular. Nevertheless, a detailed study on whether context should be incorporated implicitly (e.g. by spatial features) or explicitly (by exploiting classifiers tailored towards image analysis) and to which degree contextual information has a positive influence on the final classification result is missing in the literature. In this paper we close this gap by using projection-based Random Forests that allow to use various degrees of local context without changing the overall properties of the classifier (i.e. its capacity). Results on two PolSAR data sets - one airborne over a rural area, one space-borne over a dense urban area - show that local context indeed has substantial influence on the achieved accuracy by reducing label noise and resolving ambiguities. However, increasing access to local context beyond a certain amount has a negative effect on the obtained semantic maps.

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Authors and Affiliations

  1. SAR Technology, German Aerospace Center (DLR), Cologne, Germany

    Ronny Hänsch

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  1. Ronny Hänsch
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Correspondence to Ronny Hänsch .

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Editors and Affiliations

  1. University of Tübingen, Tübingen, Germany

    Zeynep Akata

  2. University of Tübingen, Tübingen, Germany

    Andreas Geiger

  3. Czech Technical University in Prague, Prague, Czech Republic

    Torsten Sattler

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Hänsch, R. (2021). Looking Outside the Box: The Role of Context in Random Forest Based Semantic Segmentation of PolSAR Images. In: Akata, Z., Geiger, A., Sattler, T. (eds) Pattern Recognition. DAGM GCPR 2020. Lecture Notes in Computer Science(), vol 12544. Springer, Cham. https://doi.org/10.1007/978-3-030-71278-5_19

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  • DOI: https://doi.org/10.1007/978-3-030-71278-5_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-71277-8

  • Online ISBN: 978-3-030-71278-5

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