Techniques for automated extraction of roadway inventory features from high-resolution satellite imagery

The emergence of high-resolution satellite imagery is attracting new applications which can take advantage of remotely sensed data for mapping, inventory, and change detection. Automated collection of roadway inventory features is one such application. To this end, it is important to investigate the performance of conventional feature extraction techniques when applied to high-resolution images and to develop new techniques for extraction of roadway features using one-meter, or higher, resolution imagery. In this paper, classification- based and edge detection-based techniques potential for automated extraction of roadway features from high-resolution satellite imagery are described, tested, and evaluated. Of possible techniques, the applicability of conventional classification algorithms, the Thin and Robust Zero-Crossing edge detector based on the Laplacian of Gaussian operator, and seeded region growing segmentation is investigated. The advantages and disadvantages of each technique for extracting roadway features are discussed. These techniques are applied to one-meter resolution images (currently simulated using one-meter aerial photos) and the experimental results are presented.