Linear feature extraction using perceptual grouping and graph-cuts

Abstract

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 the system execution. User intervention is allowed at any stage of the process to refine or edit the automatically generated results.