Knowledge discovery in urban environments from fused multi-dimensional imagery

With all the exciting advances in sensor fusion and data interpretation technologies in recent years, including co-registration, 3-D surface reconstruction, object recognition, spatial reasoning, and more, high-quality detailed and precise segmentation of remote sensing spectral images remains a much needed key component in the comprehensive analysis and understanding of surfaces. Urban surfaces are no exception. In fact, urban surfaces can represent more challenge than many other types because of the very large variety of materials concentrated in relatively small areas. Segmentation (unsupervised clustering) or supervised classification based on spectral signatures from multi-and hyperspectral imagery, or based on other multidimensional signatures from stacked disparate (multi-source) imagery, provide delineation of materials with various compositional and physical properties in a scene. Such a cluster or classification map lends critical support to further reasoning for accurate identification of surface objects and conditions. It is, therefore, imperative to develop methods whose data exploitation power matches that of the discriminating power of the data acquisition instrument. We present a study of unsupervised segmentation, comparing the performances of ISODATA clustering and self-organized manifold learning on an urban image from a Daedalus multi-spectral scanner and on an AVIRIS hyperspectral image.