No abstract

This work presents a novel methodology for fully automated road centerline extraction that exploits spectral content from high resolution multispectral images. Preliminary detection of candidate road centerline components is performed with Anti-parallel-edge Centerline Extraction (ACE). This is followed by constructing a road vector topology with a fuzzy grouping model that links nodes from a self-organized mapping of the ACE components. Following topology construction, a Self-Supervised Road Classification (SSRC) feedback loop is implemented to automate the process of training sample selection and refinement for a road class, as well as deriving practical spectral definitions for non-road classes. SSRC demonstrates a potential to provide dramatic improvement in road extraction results by exploiting spectral content. Road centerline extraction results are presented for three 1 m colorinfrared suburban scenes which show significant improvement following SSRC.

No abstract

From an operational standpoint, road extraction remains largely a manual process despite the existence of several commercially available automation tools. The problem of automated feature extraction (AFE) in general is a challenging task as it involves the recognition, delineation, and attribution of image features. The efficacy of AFE algorithms in operational settings is difficult to measure due to the inherent subjectivity involved. Ultimately, the most meaningful measures of an automation method are its effect on productivity and actual utility. Several quantitative and qualitative factors go into these measures including spatial accuracy and timed comparisons of extraction, different user training levels, and human-computer interface issues.In this paper we investigate methodologies for evaluating automated road extraction in different operational modes. Interactive and batch extraction modes of automation are considered. The specific algorithms investigated are the GeoEye Interactive Road Tracker ® (IRT) and the GeoEye Automated Road Tracker ® (ART) respectively. Both are commercially available from GeoEye. Analysis metrics collected are derived from timed comparisons and spatial delineation accuracy. Spatial delineation accuracy is measured by comparing algorithm output against a manually derived image reference. The effect of object-level fusion of multiple imaging modalities is also considered.The goal is to gain insight into measuring an automation algorithm's utility on feature extraction productivity. Findings show sufficient evidence to demonstrate a potential gain in productivity when using an automation method when the situation is warranted. Fusion of feature layers from multiple images also demonstrates a potential for increased productivity compared to single or pair-wise combinations of feature layers.

No abstract