&lt;title&gt;Context-driven automated target detection in 3D data&lt;/title&gt;

West, Karen F.; Webb, Brian N.; Lersch, James R.; Pothier, Steven; Triscari, Joseph; Iverson, A.

doi:10.1117/12.542536

Cited by 82 publications

(77 citation statements)

References 5 publications

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“…These features are given by linearity, planarity, sphericity, omnivariance, anisotropy, eigenentropy, sum of eigenvalues and change of curvature (West et al, 2004;Pauly et al, 2003).…”

Section: Feature Extractionmentioning

confidence: 99%

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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.

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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.

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“…These features are given by linearity, planarity, sphericity, omnivariance, anisotropy, eigenentropy, sum of eigenvalues and change of curvature (West et al, 2004;Pauly et al, 2003).…”

Section: Feature Extractionmentioning

confidence: 99%

“…Among such features, shape measures in particular are often used as they are rather intuitive and represent one single property of the local neighborhood by a single value (West et al, 2004;Jutzi and Gross, 2009;Mallet et al, 2011;Weinmann et al, 2015;Guo et al, 2015).…”

Section: Feature Extractionmentioning

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.

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“…Based on the 3D coordinates of all these 3D points, it is possible to calculate the 3D covariance matrix which is commonly referred to as the 3D structure tensor which preserves information about the local 3D structure. More specifically, the eigenvalues of the 3D structure tensor may (i) directly be exploited in order to distinguish between different shape primitives or (ii) be used in order to derive the covariance features of linearity L λ , planarity P λ , sphericity S λ , omnivariance O λ , anisotropy A λ , eigenentropy E λ and sum of eigenvalues Σ λ (West et al, 2004) and local surface variation C λ (Pauly et al, 2003). The covariance features in particular have been involved in numerous investigations, and they have partially been combined with further geometric features, e.g.…”

Section: Interpretable Featuresmentioning

confidence: 99%

“…Among the local 3D shape features, we focus on the use of covariance features (West et al, 2004;Pauly et al, 2003) which are derived from the eigenvalues of the 3D structure tensor, where…”

Section: Shape Measuresmentioning

confidence: 99%

“…Instead of using such parametric features, the second category of approaches aims to describe the local context by evaluating certain geometric measures (e.g. shape measures represented by a single value which specifies one single property) based on the whole set of 3D points within the local neighbourhood (West et al, 2004;Mallet et al, 2011;Weinmann et al, 2013;Guo et al, 2015). These metrical features are to some degree interpretable as they describe fundamental properties of the local neighbourhood.…”

Section: Introductionmentioning

confidence: 99%

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Classification of Airborne Laser Scanning Data Using Geometric Multi-Scale Features and Different Neighbourhood Types

Blomley

Jutzi

Weinmann

2016

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

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ABSTRACT:In this paper, we address the classification of airborne laser scanning data. We present a novel methodology relying on the use of complementary types of geometric features extracted from multiple local neighbourhoods of different scale and type. To demonstrate the performance of our methodology, we present results of a detailed evaluation on a standard benchmark dataset and we show that the consideration of multi-scale, multi-type neighbourhoods as the basis for feature extraction leads to improved classification results in comparison to single-scale neighbourhoods as well as in comparison to multi-scale neighbourhoods of the same type.

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Reconstruction and Analysis of 3D Scenes

Weinmann¹

2016

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<title>Context-driven automated target detection in 3D data</title>

Cited by 82 publications

References 5 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

Classification of Airborne Laser Scanning Data Using Geometric Multi-Scale Features and Different Neighbourhood Types

Reconstruction and Analysis of 3D Scenes

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