Open AccessDETECTING LINEAR FEATURES BY SPATIAL POINT PROCESSES
Author(s) -
Douglas Chai,
Ahlert Schmidt,
Christian Heipke
Publication year - 2016
Publication title -
the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.264
H-Index - 71
eISSN - 1682-1777
pISSN - 1682-1750
DOI - 10.5194/isprsarchives-xli-b3-841-2016
Subject(s) - feature (linguistics) , point (geometry) , process (computing) , computer science , linear model , pattern recognition (psychology) , artificial intelligence , term (time) , point process , linear programming , algorithm , computer vision , data mining , mathematics , machine learning , statistics , geometry , operating system , philosophy , linguistics , physics , quantum mechanics
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.National Natural Science Foundation of China/41071263National Natural Science Foundation of China/41571335Zhejiang Provincial Natural Science Foundation of China/LY13D010003Key Laboratory for National Geographic Census and Monitoring National Administration of Surveying, Mapping and Geoinformation/2014NGC