Performance evaluation and analysis of monocular building extraction from aerial imagery

Research in monocular building extraction from aerial imagery has neglected performance evaluation in three areas: unbiased metrics for quantifying detection and delineation performance, an evaluation methodology for applying these metrics to a representative body of test imagery, and an approach for understanding the impact of image and scene content on building extraction algorithms. This paper addresses these areas with an end-to-end performance evaluation of four existing monocular building extraction systems, using image space and object space-based metrics on 83 test images of 18 sites. This analysis is supplemented by an examination of the effects of image obliquity and object complexity on system performance, as well as a case study on the effects of edge fragmentation. This widely applicable performance evaluation approach highlights the consequences of various traditional assumptions about camera geometry, image content, and scene structure, and demonstrates the utility of rigorous photogrammetric object space modeling and primitive-based representations for building extraction.