The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-4/W4
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W4, 111–116, 2017
https://doi.org/10.5194/isprs-archives-XLII-4-W4-111-2017
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W4, 111–116, 2017
https://doi.org/10.5194/isprs-archives-XLII-4-W4-111-2017

  26 Sep 2017

26 Sep 2017

AUTOMATIC CENTERLINE EXTRACTION OF COVERD ROADS BY SURROUNDING OBJECTS FROM HIGH RESOLUTION SATELLITE IMAGES

H. Kamangir, M. Momeni, and M. Satari H. Kamangir et al.
  • Dept. Geomatics, Faculty of Civil Engineering and Transportation, University of Isfahan, Isfahan, Iran

Keywords: Maximum Likelihood Classification, Connected Component, RANSAC algorithm, Morphological Operators, Road Extraction, Minimum Map Unit (MMU) concept

Abstract. This paper presents an automatic method to extract road centerline networks from high and very high resolution satellite images. The present paper addresses the automated extraction roads covered with multiple natural and artificial objects such as trees, vehicles and either shadows of buildings or trees. In order to have a precise road extraction, this method implements three stages including: classification of images based on maximum likelihood algorithm to categorize images into interested classes, modification process on classified images by connected component and morphological operators to extract pixels of desired objects by removing undesirable pixels of each class, and finally line extraction based on RANSAC algorithm. In order to evaluate performance of the proposed method, the generated results are compared with ground truth road map as a reference. The evaluation performance of the proposed method using representative test images show completeness values ranging between 77% and 93%.