High resolution urban feature extraction for global population mapping using high performance computing

Abstract

The advent of high spatial resolution satellite imagery like Quick Bird (0.6 meter) and IKONOS (1 meter) has provided a new data source for high resolution urban land cover mapping. Extracting accurate urban regions from high resolution images has many applications and is essential to the population mapping efforts of Oak Ridge National Laboratory's (ORNL) LandScan population distribution program. This paper discusses an automated parallel algorithm that has been implemented on a high performance computing environment to extract urban regions from high resolution images using texture and spectral features.