Automatic building detection from very high-resolution images using multiscale morphological attribute profiles

Abstract

ABSTRACT Morphological building indexes (MBI) have proven to be effective tools for automated building spatial-feature-extraction tasks in images from urban areas. However, owing to the intrinsic shortcomings of MBI, commission and omission errors occur in regions with spectral properties similar to those of buildings and dark heterogeneous roofs, respectively. Some targets (such as bright bare land or roads) can cause substantial interference, which poses an even greater challenge in performing accurate building detection from images of complex environments. In this study, a new automated building detection approach based on a morphological attribute profile is presented with the goal of reducing commission and omission errors. As the first step, corners are detected in very high-resolution (VHR) remote sensing images through an automatic optimization procedure, and weighted spatial voting is performed to predict the presence of built-up areas. Then, by investigating the properties between the attribute filters and buildings, a novel morphological attribute building index is constructed by considering the extracted built-up area as an input image. To validate the detection performance, the approach was tested using VHR images with 1-m spatial resolution. The quantitative assessment indicates that the proposed approach improves the building detection accuracy in images of complex environments.