Linear feature extraction using perceptual grouping and graph-cuts

In this paper we present a novel system for the detection and extraction of road map information from high-resolution satellite imagery. Uniquely, the proposed system is an integrated solution that merges the power of perceptual grouping theory (gabor filtering, tensor voting) and segmentation (graph-cuts) into a unified framework to address the problems of road feature detection and classification. Local orientation information is derived using a bank of gabor filters and is refined using tensor voting. A segmentation method based on global optimization by graph-cuts is developed for segmenting foreground(road pixels) and background objects while preserving oriented boundaries. Road centerlines are detected using pairs of gaussian-based filters and road network vector maps are finally extracted using a tracking algorithm. The proposed system works with a single or multiple images, and any available elevation information. User interaction is limited and is performed at the begining of...

• Type: Conference
• Year: 2007
• Where: GIS
• Authors: Charalambos Poullis, Suya You, Ulrich Neumann
• Keywords: Database, GIS 2007, Road Feature Detection, Road Network Vector, Tensor Voting