Urban land-cover classification from High Resolution remote sensing imagery

In this paper, we consider an invariant Generalized Hough Transform (GHT) as a shape based extractor to improve the quality of the urban land-cover classification. Dense urban environment sensed by Very High-Resolution (VHR) optical sensors is one of the most challenging problems in pattern analysis and machine intelligence systems in remote sensing. We propose a three stage framework for extracting urban land-cover: a spectral cluster-based segmentation to segment and extract basic urban classes followed by two serialized classifications to extract structures of interest from the segmented data. The first classification uses Particle Swarm Optimization and shows a significant classification performance of 80-90% of roads of VHR remote sensing data over urban areas. Next, the classified data are piped into the third stage in which GHT is used to classify building areas. The suggested framework is successful in enhancing the building areas detection with an accuracy improvement of 30- 40%.