A Three-Step Approach for TLS Point Cloud Classification

Li, Zhuqiang; Zhang, Liqiang; Tong, Xiaohua; Du, Bo; Wang, Yuebin; Zhang, Liang; Zhang, Zhenxin; Liu, Hao; Mei, Jie; Xing, Xiaoyue; Mathiopoulos, P. Takis

doi:10.1109/tgrs.2016.2564501

Cited by 51 publications

(23 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To further determine the performance of the proposed MSNet, comparison experiments with other state-of-the-art methods and generalization capability analysis were conducted to accomplish these experiments; another two data sets (terrestrial laser scanning [11,15] (TLS-Wang) and ALS [21] (ALS-Zhang)) point clouds were utilized. The TLS-Wang point cloud was obtained with a single terrain scanner, in which the majority of the objects were buildings, trees, cars, and pedestrians.…”

Section: Discussionmentioning

confidence: 99%

“…It can be seen that the proposed method successfully classified most of the objects in the TLS-Wang and ALS-Zhang point clouds. For the TLS-Wang point cloud classification, we compared the proposed method to other state-of-the-art methods (sLDA model [46], LDA model [15], object-oriented decision tree [11], and PointNet++) [41]. The precision/recall of each kind of object and the overall accuracy are listed in Table 4.…”

Section: Comparision With Other Methodsmentioning

confidence: 99%

“…Similar to Li. et al [11], precision/recall were used to represent the classification quality in this paper. Quantitative evaluations of the experimental results are provided in Table 2.…”

Section: Assessmentmentioning

confidence: 99%

“…Each vertex corresponds to a point in the data, and the edges connect neighboring points [7,8]. By turning the segmentation into a min-cut/max-flow problem, the point cloud is segmented with graph cuts [9][10][11]. In addition, many studies on the graph approach also cast it into a probabilistic inference model, such as the conditional random field (CRF) [12,13], which regards the classification as a multi-labeling optimization problem.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

MSNet: Multi-Scale Convolutional Network for Point Cloud Classification

et al. 2018

View full text Add to dashboard Cite

Point cloud classification is quite challenging due to the influence of noise, occlusion, and the variety of types and sizes of objects. Currently, most methods mainly focus on subjectively designing and extracting features. However, the features rely on prior knowledge, and it is also difficult to accurately characterize the complex objects of point clouds. In this paper, we propose a concise multi-scale convolutional network (MSNet) for adaptive and robust point cloud classification. Both the local feature and global context are incorporated for this purpose. First, around each point, the spatial contexts of different sizes are partitioned as voxels of different scales. A voxel-based MSNet is then simultaneously applied at multiple scales to adaptively learn the discriminative local features. The class probability of a point cloud is predicted by fusing the features together across multiple scales. Finally, the predicted class probabilities of MSNet are optimized globally using the conditional random field (CRF) with a spatial consistency constraint. The proposed method was tested with data sets of mobile laser scanning (MLS), terrestrial laser scanning (TLS), and airborne laser scanning (ALS) point clouds. The experimental results show that the proposed method was able to achieve appreciable classification accuracies of 83.18%, 98.24%, and 97.02% on the MLS, TLS, and ALS data sets, respectively. The results also demonstrate that the proposed network has a strong generalization capability for classifying different kinds of point clouds under complex urban environments.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Comparision With Other Methodsmentioning

confidence: 99%

“…Similar to Li. et al [11], precision/recall were used to represent the classification quality in this paper. Quantitative evaluations of the experimental results are provided in Table 2.…”

Section: Assessmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

MSNet: Multi-Scale Convolutional Network for Point Cloud Classification

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Some recent approaches for point cloud classification are based on an initial over‐segmentation, for example, using supervoxels (Lim and Suter, ; Ramiya et al., ). Often, they utilise spectral information, such as RGB colour values (Li et al., ; Ramiya et al., ). Spatial contextual reasoning is used in the computer vision and robotics community to classify or interpret 3D point clouds (Hu et al., ; Shapovalov et al., ).…”

Section: Introduction and Related Workmentioning

confidence: 99%

Object‐based classification of terrestrial laser scanning point clouds for landslide monitoring

Mayr

Rutzinger

Bremer

et al. 2017

The Photogrammetric Record

View full text Add to dashboard Cite

Terrestrial laser scanning (TLS) is often used to monitor landslides and other gravitational mass movements with high levels of geometric detail and accuracy. However, unstructured TLS point clouds lack semantic information, which is required to geomorphologically interpret the measured changes. Extracting meaningful objects in a complex and dynamic environment is challenging due to the objects' fuzziness in reality, as well as the variability and ambiguity of their patterns in a morphometric feature space. This work presents a point-cloud-based approach for classifying multitemporal scenes of a hillslope affected by shallow landslides. The 3D point clouds are segmented into morphologically homogeneous and spatially connected parts. These segments are classified into seven target classes (scarp, eroded area, deposit, rock outcrop and different classes of vegetation) in a two-step procedure: a supervised classification step with a machine-learning classifier using morphometric features, followed by a correction step based on topological rules. This improves the final object extraction considerably.

show abstract

Segmentation-Based 3D Point Cloud Classification on a Large-Scale and Indoor Semantic Segmentation Dataset

Sağlam

Baykan

2021

Innovations in Smart Cities Applications Volume 4

View full text Add to dashboard Cite

A Three-Step Approach for TLS Point Cloud Classification

Cited by 51 publications

References 33 publications

MSNet: Multi-Scale Convolutional Network for Point Cloud Classification

MSNet: Multi-Scale Convolutional Network for Point Cloud Classification

Object‐based classification of terrestrial laser scanning point clouds for landslide monitoring

Segmentation-Based 3D Point Cloud Classification on a Large-Scale and Indoor Semantic Segmentation Dataset

Contact Info

Product

Resources

About