The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Download
Publications Copernicus
Download
Citation
Articles | 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
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 841–848, 2016
https://doi.org/10.5194/isprs-archives-XLI-B3-841-2016

  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.