Multilevel object-oriented classification of quickbird images for urban population estimates

This paper is committed to explore object-oriented methods for the classification of Quickbird images, aiming to support future urban population estimates. The study area concerns the southern sector of Sao Jose dos Campos city, located in the State of Sao Paulo, Brazil. By means of a multi-resolution segmentation approach and a six-layer hierarchical classification network, homogeneous residential areas were identified in terms of density of occupation and building standards (single dwelling units or high-rise buildings). The classification network was built upon spectral, geometrical and topological features of the objects in each level of segmentation as well as upon their contextual and semantic interrelationships in-between the hierarchical levels. The final classification of homogeneous residential units was subject to validation, using an object-based Kappa statistics.