Detection and Classification of Buildings by Height from Single Urban High-Resolution Remote Sensing Images

Abstract

Recent improvements in remote sensing technologies have boosted building detection techniques from rough classifications using moderate resolution imagery to precise extraction from high-resolution imagery. Shadows frequently emerge in high-resolution urban images. To exploit shadow information, we developed a novel building detection and classification algorithm for images of urban areas with large-size shadows, employing only the visible spectral bands to determine the height levels of buildings. The proposed method, building general-classified by height (BGCH), calculates shadow orientation, detects buildings using seed-blocks, and classifies the buildings into different height groups. Our proposed approach was tested on complex urban scenes from Toronto and Beijing. The experimental results illustrate that our proposed method accurately and efficiently detects and classifies buildings by their height levels; the building detection rate exceeded 95%. The precision of classification by height levels was over 90%. This novel building-height-level detection method provides rich information at low cost and is suitable for further city scene analysis, flood disaster risk assessment, population estimation, and building change detection applications.