Land Cover Classification in a Complex Urban-Rural Landscape with QuickBird Imagery

High spatial resolution images have been increasingly used for urban land-use/land-cover classification, but the high spectral variation within the same land-cover, the spectral confusion among different land-covers, and the shadow problem often lead to poor classification performance based on the traditional per-pixel spectral-based classification methods. This paper explores approaches to improve urban land-cover classification with Quick Bird imagery. Traditional per-pixel spectral-based supervised classification, incorporation of textural images and multispectral images, spectral-spatial classifier, and segmentation-based classification are examined in a relatively new developing urban landscape, Lucas do Rio Verde in Mato Grosso State, Brazil. This research shows that use of spatial information during the image classification procedure, either through the integrated use of textural and spectral images or through the use of segmentation-based classification method, can significantly improve land-cover classification performance.