3D Semantic labeling of ALS point clouds by exploiting multi-scale, multi-type neighborhoods for feature extraction

Blomley, Rosmarie; Jutzi, Boris; Weinmann, Martin

doi:10.3990/2.418

Cited by 13 publications

(12 citation statements)

References 34 publications

(42 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Particularly the classes Low Vegetation, Shrub and Fence / Hedge exhibit a similar geometric behavior and misclassifications among these classes therefore occur quite often. However, this is in accordance with other investigations involving the Vaihingen Dataset (Blomley et al, 2016a;Steinsiek et al, 2017). Furthermore, the classes Powerline and Car reveal lower detection rates, which is also due to the fact that these classes are not covered representatively in the training data, where they are represented by 546 and 4614 examples, respectively.…”

Section: Discussionsupporting

confidence: 82%

“…However, only few approaches have been evaluated on the provided dataset so far Blomley et al, 2016a;Steinsiek et al, 2017), and correctly classifying the dataset turned out to be rather challenging as several classes reveal a quite similar geometric behavior (e.g. the classes Low Vegetation, Fence / Hedge and Shrub), while others combine subgroups of different appearance (e.g.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Using Multi-Scale Features for the 3d Semantic Labeling Of Airborne Laser Scanning Data

Blomley

Weinmann

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

Self Cite

View full text Add to dashboard Cite

ABSTRACT:In this paper, we present a novel framework for the semantic labeling of airborne laser scanning data on a per-point basis. Our framework uses collections of spherical and cylindrical neighborhoods for deriving a multi-scale representation for each point of the point cloud. Additionally, spatial bins are used to approximate the topography of the considered scene and thus obtain normalized heights. As the derived features are related with different units and a different range of values, they are first normalized and then provided as input to a standard Random Forest classifier. To demonstrate the performance of our framework, we present the results achieved on two commonly used benchmark datasets, namely the Vaihingen Dataset and the GML Dataset A, and we compare the results to the ones presented in related investigations. The derived results clearly reveal that our framework excells in classifying the different classes in terms of pointwise classification and thus also represents a significant achievement for a subsequent spatial regularization.

show abstract

Section: Discussionsupporting

confidence: 82%

Section: Introductionmentioning

confidence: 99%

Using Multi-Scale Features for the 3d Semantic Labeling Of Airborne Laser Scanning Data

Blomley

Weinmann

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The WhuY3 8 deep learning method used a convolutional network operating on four features in a multi-scale fashion. The K LDA (Blomley et al, 2016) method used covariance features at multiple scales. Finally, the LUH 9 method used a two-layer hierarchical CRF that explicitly defines contextual relationships and utilizes voxel cloud connectivity segmentation, along with handcrafted features such as Fast Point Feature Histograms (FPFH).…”

Section: Classification Resultsmentioning

confidence: 99%

“…Labeling was achieved by filtering the scene into ground and non-ground points according to Axelsson (2000), then applying a 3D-region-growing segmentation to both sets to generate object proposals. Like Blomley et al (2016), several geometric features were also derived, although specific details were not published. Without incorporating contextual features, each segment/proposal was then classified into a selected set of five classes from the main ISPRS 3D Semantic Labeling Contest.…”

Section: Direct Methodsmentioning

confidence: 99%

A multi-scale fully convolutional network for semantic labeling of 3D point clouds

Yousefhussien

Kelbe

Ientilucci

et al. 2018

ISPRS Journal of Photogrammetry and Remote Sensing

105

View full text Add to dashboard Cite

When classifying point clouds, a large amount of time is devoted to the process of engineering a reliable set of features which are then passed to a classifier of choice. Generally, such features -usually derived from the 3Dcovariance matrix -are computed using the surrounding neighborhood of points. While these features capture local information, the process is usually time-consuming, and requires the application at multiple scales combined with contextual methods in order to adequately describe the diversity of objects within a scene. In this paper we present a 1D-fully convolutional network that consumes terrain-normalized points directly with the corresponding spectral data, if available, to generate point-wise labeling while implicitly learning contextual features in an end-to-end fashion. Our method uses only the 3D-coordinates and three corresponding spectral features for each point. Spectral features may either be extracted from 2D-georeferenced images, as shown here for Light Detection and Ranging (LiDAR) point clouds, or extracted directly for passive-derived point clouds, i.e. from muliple-view imagery. We train our network by splitting the data into square regions, and use a pooling layer that respects the permutation-invariance of the input points. Evaluated using the ISPRS 3D Semantic Labeling Contest, our method scored second place with an overall accuracy of 81.6%. We ranked third place with a mean F1-score of 63.32%, surpassing the F1-score of the method with highest accuracy by 1.69%. In addition to labeling 3D-point clouds, we also show that our method can be easily extended to 2D-semantic segmentation tasks, with promising initial results.

show abstract

“…In recent decades, airborne laser scanning (ALS) has become important in acquiring 3D point clouds. ALS point clouds allow an automated analysis of large areas in terms of assigning a (semantic) class label to each point of the considered 3D point clouds (Blomley et al, 2016;Chehata et al, 2009;Mallet et al, 2011;Niemeyer et al, 2014;Shapovalov et al, 2010). However, the relatively low point density, the irregular point distribution and the complexity of observed scenes make the accuracy of the classification result hard to improve.…”

Section: Introductionmentioning

confidence: 99%

Eigenentropy Based Convolutional Neural Network Based Als Point Clouds Classification Method

Zheng

2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:The classification of point clouds is the first step in the extraction of various types of geo-information form point clouds. Recently the ISPRS WG II/4 provides a benchmark on 3D semantic labelling, a convolutional neural network based method achieves the best overall accuracy performance in all participants who only use the geometrical and waveform based features extracted from the ALS data. Features of the point are calculated in different scales to achieve the best performance. It is not efficiency for the future use. In this paper, we use an eigenentropy based scale selection strategy to improve this method. The scale selection strategy improves the average F1 score and makes the classification method more simple and efficient.

show abstract

3D Semantic labeling of ALS point clouds by exploiting multi-scale, multi-type neighborhoods for feature extraction

Cited by 13 publications

References 34 publications

Using Multi-Scale Features for the 3d Semantic Labeling Of Airborne Laser Scanning Data

Using Multi-Scale Features for the 3d Semantic Labeling Of Airborne Laser Scanning Data

A multi-scale fully convolutional network for semantic labeling of 3D point clouds

Eigenentropy Based Convolutional Neural Network Based Als Point Clouds Classification Method

Contact Info

Product

Resources

About