Tian Zeyu, Fang Yong, Fang Xiaohui, Ma Yan, Li Han
State Key Laboratory of Geo-Information Engineering, Xi'an Research Institute of Surveying and Mapping, Xi'an 710054, China.
College of Surveying and Mapping, Heilongjiang Institute of Technology, Harbin 150050, China.
Sensors (Basel). 2024 Nov 25;24(23):7503. doi: 10.3390/s24237503.
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.
从激光雷达点云与遥感影像中自动进行大规模建筑物提取，是传感器应用和遥感领域日益关注的焦点。然而，由于建筑物尺寸、形状及周边环境的复杂性，该建筑物提取任务仍极具挑战性。此外，点云的离散性、稀疏性和不规则分布、光照及阴影，以及影像的遮挡，也严重影响建筑物提取的准确性。为解决上述问题，我们基于一种新的双P-蛇模型（双点与像素蛇模型）提出了一种新的无监督建筑物提取算法PBEA（点与像素建筑物提取算法）。所提出的双P-蛇模型是一种增强型主动边界模型，它同时利用点云和影像来获取内边界和外边界。所提出的双P-蛇模型使内边界和外边界之间能够相互作用并收敛，以提高建筑物边界检测的性能，尤其是在复杂场景中。利用双P-蛇模型和多边形化，所提出的PBEA能够准确提取大规模建筑物。我们在ISPRS维亨根数据集和多伦多数据集上对我们的PBEA和双P-蛇模型进行了评估。实验结果表明，我们的PBEA在维亨根数据集上基于面积的质量评估指标达到了90.0%，在多伦多数据集上基于面积的质量评估指标达到了92.4%。与其他方法相比，我们的方法表现出令人满意的性能。
