The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLI-B3
https://doi.org/10.5194/isprs-archives-XLI-B3-841-2016
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 Chai, Alena Schmidt, and Christian Heipke

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.