Automated Urban Delineation from Landsat Imagery Based on Spatial Information Processing

Abstract

Methodologies are developed and assessed for the delineation of urban areas on moderate resolution satellite images based on line feature extraction and processing. It is argued that a simple two component urban/rural road network model and a preliminary estimate of the extent of urban cover in a satellite scene can be used to quantify processing settings, such as line thresholds. As a result, urban delineation can be fully automated. Tests have been conducted with Landsat Thematic Mapper data of a candidate set of cities that exemplify the spectrum of North American urban landscapes. Producer accuracies in the range 60 to 80 percent are consistently for a variety of urban landscapes. While these performance levels are lower than those previously reported for small area, city-specific studies, they are comparable to those previously achieved in large-area, approaches (e.g., the National Land Cover Dataset initiative) that rely primarily on spectral attributes. It is concluded that better integrated use of spectral-spatial data processing have the potential to lead to improved operational urban mapping accuracies.