Volume XLI-B3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 841-848, 2016
https://doi.org/10.5194/isprs-archives-XLI-B3-841-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 841-848, 2016
https://doi.org/10.5194/isprs-archives-XLI-B3-841-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  10 Jun 2016

10 Jun 2016

DETECTING LINEAR FEATURES BY SPATIAL POINT PROCESSES

Dengfeng Chai1, Alena Schmidt2, and Christian Heipke2 Dengfeng Chai et al.
  • 1Institute of Spatial Information Technique, Zhejiang University, China
  • 2Institute of Photogrammetry and GeoInformation, Leibniz Universit¨at Hannover, Germany

Keywords: Linear Feature, Feature Detection, Spatial Point Processes, Global Optimization, Simulated Annealing, Markov Chain Monte Carlo

Abstract. This paper proposes a novel approach for linear feature detection. The contribution is twofold: a novel model for spatial point processes and a new method for linear feature detection. It describes a linear feature as a string of points, represents all features in an image as a configuration of a spatial point process, and formulates feature detection as finding the optimal configuration of a spatial point process. Further, a prior term is proposed to favor straight linear configurations, and a data term is constructed to superpose the points on linear features. The proposed approach extracts straight linear features in a global framework. The paper reports ongoing work. As demonstrated in preliminary experiments, globally optimal linear features can be detected.