Advanced directional mathematical morphology for the detection of the road network in very high resolution remote sensing images

Authors:
S. Valero;J. Chanussot;J. A. Benediktsson;H. Talbot;B. Waske
Affiliations:
GIPSA-lab, Signal and Image Dept., Grenoble Institute of Technology, Grenoble, France and Faculty of Electrical and Computer Engineering, University of Iceland, Reykjavik, Iceland;GIPSA-lab, Signal and Image Dept., Grenoble Institute of Technology, Grenoble, France;Faculty of Electrical and Computer Engineering, University of Iceland, Reykjavik, Iceland;IGM-A2SI-ESIEE, BP 99-2 Bd., Blaise-Pascal, 93162 Noisy-le-Grand, France;Faculty of Electrical and Computer Engineering, University of Iceland, Reykjavik, Iceland
Venue:
Pattern Recognition Letters
Year:
2010

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Abstract

Very high spatial resolution (VHR) images allow to feature man-made structures such as roads and thus enable their accurate analysis. Geometrical characteristics can be extracted using mathematical morphology. However, the prior choice of a reference shape (structuring element) introduces a shape-bias. This paper presents a new method for extracting roads in Very High Resolution remotely sensed images based on advanced directional morphological operators. The proposed approach introduces the use of Path Openings and Path Closings in order to extract structural pixel information. These morphological operators remain flexible enough to fit rectilinear and slightly curved structures since they do not depend on the choice of a structural element shape. As a consequence, they outperform standard approaches using rotating rectangular structuring elements. The method consists in building a granulometry chain using Path Openings and Path Closing to construct Morphological Profiles. For each pixel, the Morphological Profile constitutes the feature vector on which our road extraction is based.