Point Cloud Classification by Fusing Supervoxel Segmentation With Multi-Scale Features

Abstract

<p><strong>Abstract.</strong> Point cloud classification is quite a challenging task due to the existence of noises, occlusion and various object types and sizes. Currently, the commonly used statistics-based features cannot accurately characterize the geometric information of a point cloud. This limitation often leads to feature confusion and classification mistakes (e.g., points of building corners and vegetation always share similar statistical features in a local neighbourhood, such as curvature, sphericity, etc). This study aims at solving this problem by leveraging the advantage of both the supervoxel segmentation and multi-scale features. For each point, its multi-scale features within different radii are extracted. Simultaneously, the point cloud is partitioned into simple supervoxel segments. After that, the class probability of each point is predicted by the proposed SegMSF approach that combines multi-scale features with the supervoxel segmentation results. At the end, the effect of data noises is supressed by using a global optimization that encourages spatial consistency of class labels. The proposed method is tested on both airborne laser scanning (ALS) and mobile laser scanning (MLS) point clouds. The experimental results demonstrate that the proposed method performs well in terms of classifying objects of different scales and is robust to noise.</p>

Mago Approach for Semantic Segmentation: The Case Study of Uavid Benchmark Dataset

“…This approach is widely used in remote sensing applications, especially in the analysis of urban scenarios (Ajmar et al, 2019;Huang et al, 2019, Schmitz et al, 2019, Zhou et al, 2019 or in the delineation of forest trees (Chen et al, 2021;Sothe et al, 2020;Kempf et al, 2019). The segmentation approach could be based on imagery (Marmanis et al, 2018) or three-dimensional models (Ao et al, 2019), as well as on the combination of both 2D and 3D information (Ding et al, 2019). Typically, deep learning methods are applied to such procedure, including, to cite some examples, Conditional Random Fields (CRF; Pan et al, 2020;Lafferty et al, 2001), Markov Random Fields (MRF; Zoltan and Josiane, 2012), Spatial Pyramid Pooling (SPP; Zhengyu and Joohee, 2020), and Convolutional Neural Networks (Cresson, 2020;Martinez-Soltero et al, 2020;Ouyang and Li, 2021).…”