Classification of remote sensing images from urban areas using Laplacian image and Bayesian theory

This paper presents the methodology of urban area classification in high resolution satellite IKONOS imagery. The strategies include building extraction by Bayesian theory and laplacian criterion, labeling and size filtering, intensity threshold and etc which are applied to IKONOS image in tandem to make this algorithm as an effective strategy to save processing time and improve robustness. To realize the strategy, First, vegetation are extracted in attend to green layer of RGB image then buildings are detected by Bayesian decision theory in regard to laplacian probability density function, then shadows which have low intensity are detected. In the next step a special intensity level is calculated as a threshold level to discern roads. Finally open areas are extracted from remained of image as regions with low laplacian intensity and large size. Meanwhile morphological operations are applied to remove redundant image's particles. Experimental result indicates that this approach has a high efficiency especially in extraction of large roads and streets from dense urban area IKNOS images.