Pattern-Based Accuracy Assessment of an Urban Footprint Classification Using TerraSAR-X Data

Abstract

Assessing the accuracy of land-cover classifications is a major challenge in remote sensing. This is mostly due to the absence of geometrically and thematically highly resolved, reliable, area wide, and up-to-date reference data. This study focuses on a multifaceted accuracy assessment of an urban footprint classification derived from a single-polarized TerraSAR-X image in stripmap mode for the city of Padang in Indonesia. For this purpose, a pixel-based approach was used to identify the urbanized and nonurbanized areas. As reference, a geometrically and thematically highly resolved, accurate, and detailed 3-D city model is available. Based on this data, the classification result is assessed by basic methodologies—square measures and error matrix. Beyond that, the accuracy of the urban footprint classification is analyzed in dependence of the physical structure of the complex urban landscape—defined by built-up density and building volumes. Results reveal that the accuracy of classification results varies in dependence of the structural characteristics of the particular urban environment. Furthermore, the study shows what is thematically mapped by an urban footprint classification.