A Multifeature Fusion Framework Based on D-S Theory for Automatic Building Extraction From High-Resolution Remote Sensing Imagery

Building information serves as a critical foundational dataset in the fields of urban planning, smart cities and surveying and mapping, and high-resolution remote sensing (HRRS) imagery has become a vital data source for extracting building information. However, automatically extracting building information from HRRS imagery using a single feature or method remains a challenging task. On one hand, buildings exhibit significant variations in terms of their size, color, geometric structures, and other aspects. On the other hand, there are also numerous features in the environment that bear spectral and morphological resemblances to buildings. In this article, we proposed a multifeature fusion framework based on Dempster–Shafer (D-S) theory that consists of two steps for automated building extraction from HRRS imagery. The initial D-S fusion step involves two branches: object-level and pixel-level feature fusion. Then the outcomes are further combined to derive the ultimate building confidence information. In the framework, we introduced a proportional consistency and centroid consistency index to convert pixel-level features to object-level features, thereby facilitating their fusion. In addition, we proposed an initialization module for the basic probability assignment formula, enabling the elimination of the impact of nonbuilding objects and simplifying the construction process of BPAF. The experimental results based on the Nanjing, WHU, and Washington datasets demonstrate the effectiveness of our method, the accuracy outperforms the other four advanced algorithms.