Classification and material identification in an urban environment using HYDICE hyperspectral data

Urban areas provide a complex material environment, both in the number of materials present, and in the spatial scale of material variation. Classification in urban environments using multispectral sensors has typically been limited to discrimination of major terrain classes due to both the limited spatial resolution of currently available sensors and to the inability to consistently discriminate between similar materials. High spectral and spatial resolution imagery, such as collected with the HYDICE sensor, provides the opportunity to develop detailed material maps for urban areas, and to perform precise material discrimination for cultural objects. Referencing a comprehensive set of material spectra, this paper describes a procedure for land cover classification which can be automated and performed with little or no a-priori knowledge of objects in the scene.