Automatic extraction of built-up area from ZY3 multi-view satellite imagery: Analysis of 45 global cities

Abstract

Accurate delineation of global built-up area (BUA) is fundamental to a better understanding of human development and the impacts on global environmental change. Existing global datasets of human settlement were mostly generated at medium and coarse spatial resolutions, including BUA and other impervious surfaces. With multiple high-resolution satellite constellations now available (e.g., ZiYuan-3 (ZY3), SPOT-5/6/7, Cartosat-1/2, and WorldView-2/3), identifying the global BUA explicitly from the complex landscapes becomes possible. In this study, a novel method was proposed for automated extraction of BUA at the global scale, by fusing a series of building features. Specifically, two planar features, the Morphological Building Index (MBI) and Harris corner detector, were employed to characterize the structure and corner attributes of buildings. Moreover, two multi-angular built-up indices (MABIs), i.e., Ratio Multi-angular Built-up Index (RMABI) and Normalized Difference Multi-angular Built-up Index (NDMABI), were proposed to represent the vertical properties of buildings based on multi-view images, which can further complement the planar features. 45 global cities were selected to validate the performance of the proposed method with images acquired by the ZY3 satellite constellation. The results show that the fusion of MBI and Harris corner can achieve satisfactory accuracy, i.e., 91.12%, 88.85%, 82.82% and 0.85, for the average overall accuracy (OA), user's accuracy (UA), producer's accuracy (PA), and F1-score, respectively, for all the test cities. After fusing the MABIs with the planar features, the average OA, UA, PA and F values of the final results were 92.00%, 86.20%, 89.14% and 0.87 for the RMABI, and 91.83%, 85.51%, 89.62% and 0.87 for the NDMABI, respectively. In particular, addition of the MABIs can further reduce the omission errors where medium/high rise buildings with low local contrast exist. We compared our results with two existing state-of-the-art global BUA products, Global Human Settlement Layer (GHSL) and Global Urban Footprint (GUF), which further corroborated the effectiveness of our method.