An Improved Boosting Learning Saliency Method for Built-Up Areas Extraction in Sentinel-2 Images

Abstract

Built-up areas extraction from satellite images is an important aspect of urban planning and land use; however, this remains a challenging task when using optical satellite images. Existing methods may be limited because of the complex background. In this paper, an improved boosting learning saliency method for built-up area extraction from Sentinel-2 images is proposed. First, the optimal band combination for extracting such areas from Sentinel-2 data is determined; then, a coarse saliency map is generated, based on multiple cues and the geodesic weighted Bayesian (GWB) model, that provides training samples for a strong model; a refined saliency map is subsequently obtained using the strong model. Furthermore, cuboid cellular automata (CCA) is used to integrate multiscale saliency maps for improving the refined saliency map. Then, coarse and refined saliency maps are synthesized to create a final saliency map. Finally, the fractional-order Darwinian particle swarm optimization algorithm (FODPSO) is employed to extract the built-up areas from the final saliency result. Cities in five different types of ecosystems in China (desert, coastal, riverside, valley, and plain) are used to evaluate the proposed method. Analyses of results and comparative analyses with other methods suggest that the proposed method is robust, with good accuracy.