An ALTM digital height model associated with VHR imagery for an object-based classification of intra-urban targets

The study of the urban environment has raised great interest among researchers and practitioners involved with the use of remote sensing, in face of the challenges for its investigation, like the fast and ongoing changes of its structure and the complexity of its targets. New concepts and analyses have been used for mapping the urban space. Object-based analysis and multi-resolution segmentation have been quite efficient in the discrimination of urban targets in high spatial resolution images. In this context, this paper proposes a methodology employing cognitive approaches for the classification of land cover in urban areas using optical orbital and airborne laser scanning data. The results were presented and discussed, indicating a satisfactory accuracy in the generated mapping products, demonstrating the reliability of the methodology for mapping urban land cover.