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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.

• A multifeature fusion model was proposed for online surface roughness measurement of cold-rolled strip steel. • The statistical and depth features of speckle images were extracted to evaluate the surface roughness of cold-rolled strip steel. • Convolutional neural networks and an attention mechanism module were adopted to extract the depth features of the speckle images. • A support vector regression algorithm was used to predict the surface roughness. The online measurement of the surface roughness of cold-rolled strip steel plays a significant role in the steel manufacturing process. However, the traditional mechanism method based on laser speckle is not sufficient for image processing, and the precision of online measurement is not high. Therefore, this paper proposes a multifeature fusion model for the surface roughness of cold-rolled strip steel to improve the measurement efficiency of existing speckle methods. Cold-rolled strip steel is irradiated by a laser beam to produce speckle images. Statistical features are extracted using a gray-level cooccurrence matrix (GLCM). The deep information of an image is extracted using convolutional neural networks (CNN) and a convolutional block attention mechanism module (CBAM). The features extracted from the GLCM and CNN-CBAM methods are combined to create a multifeature fusion dataset. The fused features are predicted by a support vector regression (SVR) algorithm. Then, the SVR model is compared with three machine learning prediction models: classification and regression tree (CART), random forest (RF), and k-nearest neighbor (KNN). Experimental results confirm that the proposed multifeature fusion model predicts surface roughness with a minimum mean square error of 0.237%. [ABSTRACT FROM AUTHOR]