Image Homogeneity and Urban Demographics: An Integrated Approach to Applied Geo-techniques

Satellite images are a convenient and effective tool to measure landscape patterns because they provide a digital mosaic of the spatial arrangement of land covers (Chuvieco, 1999). However, satellite remote sensing data have not been applied to the urban landscape to the same degree that they have to other landscapes because the ability to discern urban features from satellite data is often very difficult since urban landscapes are composed of a very diverse assemblage of anthropogenic and natural materials (Mesev and Longley, 1999; Jensen, 2000). For example, a typical urban setting may have concrete, asphalt, plastic, metal, water, grass, shrubbery, trees, and soil. In fact, even when high spatial resolution data is used, conventional classification techniques have proven ineffective in urban areas (Gong and Howarth, 1992; Anys et al., 1998). Part of the problem with traditional urban classifications may be the single-pixel basis on which conventional classifications rely (Karathanassi et al., 2000). Indeed, the classification of remote sensing data relies on the assumption that the area being classified is composed of unique, internally homogeneous classes (Zhang and Foody, 1998). Therefore, significant progress in urban remote sensing will require novel methods to measure, model, and understand the dynamic nature of urban areas (Longley, 2002). One of these new methods may involve the use of image texture.