Automatic large-scale building extraction from the LiDAR point clouds and remote sensing images is a growing focus in the fields of the sensor applications and remote sensing. However, this building extraction task remains highly challenging due to the complexity of building sizes, shapes, and surrounding environments. In addition, the discreteness, sparsity, and irregular distribution of point clouds, lighting, and shadows, as well as occlusions of the images, also seriously affect the accuracy of building extraction. To address the above issues, we propose a new unsupervised building extraction algorithm PBEA (Point and Pixel Building Extraction Algorithm) based on a new dual P-snake model (Dual Point and Pixel Snake Model). The proposed dual P-snake model is an enhanced active boundary model, which uses both point clouds and images simultaneously to obtain the inner and outer boundaries. The proposed dual P-snake model enables interaction and convergence between the inner and outer boundaries to improve the performance of building boundary detection, especially in complex scenes. Using the dual P-snake model and polygonization, this proposed PBEA can accurately extract large-scale buildings. We evaluated our PBEA and dual P-snake model on the ISPRS Vaihingen dataset and the Toronto dataset. The experimental results show that our PBEA achieves an area-based quality evaluation metric of 90.0% on the Vaihingen dataset and achieves the area-based quality evaluation metric of 92.4% on the Toronto dataset. Compared with other methods, our method demonstrates satisfactory performance.

Let the computer apprehend and describe the representation of the relative position between objects of the image by the way of the common intuition of the human is an important task of the computer vision and pattern recognition. To complete this task, the position sensing parameter and histogram of position sensing forces are proposed in this paper. The position sensing parameter can represent the spatial relative position of the point with regard to the reference object, and the point is from the argument object. The histogram of position sensing forces is composed of the position sensing parameter of each point in the argument object and the gravitational forces between each point of the argument object and each point of the reference object. The histogram of position sensing forces can simulate the human perception for the directional spatial relations between the argument object and reference object of the image, considering the shape, size, angular and metric information of the spatial object.

With the rapid development of airborne Light Detection and Ranging (LiDAR) sensors, point clouds have been widely applied in fields such as surveying and mapping engineering, intelligent monitoring, and autonomous driving. However, the irregular structure of point clouds seriously affects the segmentation, recognition, and understanding of point clouds. To address the challenge, this paper proposes a spatial feature structure generation network (SFSGNet). The spatial feature structure generation (SFSG) module can deeply fuse attribute features and spatial coordinate features to generate highly expressive spatial structure features in both semantic and spatial contexts, and through the spatial structure features, SFSGNet can effectively handle irregular point clouds, describe the structure of point cloud objects, and achieve accurate point cloud semantic segmentation. We evaluate our SFSGNet using the ISPRS 3D Labeling Benchmark dataset, DALES dataset and OpenGF dataset. Experimental results show that our SFSGNet achieves an average F1 score 74.4% and overall accuracy 85.4% on the ISPRS dataset, achieves an average F1 score 89.3% and overall accuracy 98.1% on the DALES dataset, and achieves an average F1 score 98.2% and overall accuracy 98.5% on the OpenGF dataset. Compared with other representative models, our SFSGNet demonstrates excellent segmentation performance and strong generalization.