Hierarchical graph-based segmentation for extracting road networks from high-resolution satellite images

Rasha Alshehhi, Prashanth Reddy Marpu

Research output: Contribution to journalArticlepeer-review


Extraction of road networks in urban areas from remotely sensed imagery plays an important role in many urban applications (e.g. road navigation, geometric correction of urban remote sensing images, updating geographic information systems, etc.). It is normally difficult to accurately differentiate road from its background due to the complex geometry of the buildings and the acquisition geometry of the sensor. In this paper, we present a new method for extracting roads from high-resolution imagery based on hierarchical graph-based image segmentation. The proposed method consists of: 1. Extracting features (e.g., using Gabor and morphological filtering) to enhance the contrast between road and non-road pixels, 2. Graph-based segmentation consisting of (i) Constructing a graph representation of the image based on initial segmentation and (ii) Hierarchical merging and splitting of image segments based on color and shape features, and 3. Post-processing to remove irregularities in the extracted road segments. Experiments are conducted on three challenging datasets of high-resolution images to demonstrate the proposed method and compare with other similar approaches. The results demonstrate the validity and superior performance of the proposed method for road extraction in urban areas.

Original languageEnglish (US)
Pages (from-to)245-260
Number of pages16
JournalISPRS Journal of Photogrammetry and Remote Sensing
StatePublished - Apr 1 2017


  • Gabor
  • Graph-based segmentation
  • Hierarchical merging and splitting
  • Morphological filtering
  • Road networks

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Engineering (miscellaneous)
  • Computer Science Applications
  • Computers in Earth Sciences


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