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

Blomley, Rosmarie; Weinmann, Martin

doi:10.5194/isprs-annals-iv-2-w4-43-2017

Cited by 25 publications

(24 citation statements)

References 34 publications

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“…The neighborhood types and scales are the two key elements for the local neighborhood. According to the previous studies [9,12,42,59,60], the commonly used neighborhood types are spherical, vertical cylindrical, k-nearest, optimal k-nearest, and slant cylindrical neighborhoods, while the neighborhood scale could be usually divided into single scale and multi-scale types.…”

Section: Sensitivity Analysis Of Local Neighborhoodmentioning

confidence: 99%

Systematic Comparison of Power Line Classification Methods from ALS and MLS Point Cloud Data

et al. 2018

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Power lines classification is important for electric power management and geographical objects extraction using LiDAR (light detection and ranging) point cloud data. Many supervised classification approaches have been introduced for the extraction of features such as ground, trees, and buildings, and several studies have been conducted to evaluate the framework and performance of such supervised classification methods in power lines applications. However, these studies did not systematically investigate all of the relevant factors affecting the classification results, including the segmentation scale, feature selection, classifier variety, and scene complexity. In this study, we examined these factors systematically using airborne laser scanning and mobile laser scanning point cloud data. Our results indicated that random forest and neural network were highly suitable for power lines classification in forest, suburban, and urban areas in terms of the precision, recall, and quality rates of the classification results. In contrast to some previous studies, random forest yielded the best results, while Naïve Bayes was the worst classifier in most cases. Random forest was the more robust classifier with or without feature selection for various LiDAR point cloud data. Furthermore, the classification accuracies were directly related to the selection of the local neighborhood, classifier, and feature set. Finally, it was suggested that random forest should be considered in most cases for power line classification.

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Section: Sensitivity Analysis Of Local Neighborhoodmentioning

confidence: 99%

Systematic Comparison of Power Line Classification Methods from ALS and MLS Point Cloud Data

et al. 2018

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“…A straightforward extension would be the extraction of geometric features at multiple scales [5,10,20] and possibly different neighborhood types [11,22]. Furthermore, more complex geometric features could be considered [11] or deep learning techniques could be applied to learn appropriate features from 3D data [6].…”

Section: Featurementioning

confidence: 99%

“…In this regard, one may use multiple spherical neighborhoods [20], multiple cylindrical neighborhoods [10,21], or a multi-scale voxel representation [5]. Furthermore, multiple neighborhoods could be defined on the basis of different entities, e.g., in the form of both spherical and cylindrical neighborhoods [11], in the form of voxels, blocks, and pillars [22], or in the form of spatial bins, planar segments, and local neighborhoods [23].…”

Section: Neighborhood Selectionmentioning

confidence: 99%

“…Among a range of addressed research topics, particular attention has been paid to the semantic interpretation of 3D data acquired via MLS, TLS, or ALS systems within urban areas [1][2][3][4][6][7][8][9][10][11] which is an important prerequisite for a variety of high-level tasks such as city modeling and planning. In such a scenario, the acquired 3D data corresponds to a dense sampling of object surfaces preserving many details of the geometric structure.…”

Section: Related Workmentioning

confidence: 99%

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Geospatial Computer Vision Based on Multi-Modal Data—How Valuable Is Shape Information for the Extraction of Semantic Information?

Weinmann

2017

Remote Sensing

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Abstract:In this paper, we investigate the value of different modalities and their combination for the analysis of geospatial data of low spatial resolution. For this purpose, we present a framework that allows for the enrichment of geospatial data with additional semantics based on given color information, hyperspectral information, and shape information. While the different types of information are used to define a variety of features, classification based on these features is performed using a random forest classifier. To draw conclusions about the relevance of different modalities and their combination for scene analysis, we present and discuss results which have been achieved with our framework on the MUUFL Gulfport Hyperspectral and LiDAR Airborne Data Set.

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“…To combine both aspects, we use two line scanners, one scanning along the vertical direction and one scanning along the horizontal direction with respect to the UAV. To analyze the acquired data, we rely on the use of geometric features which are typically involved in the interpretation of ALS data (Niemeyer et al, 2014;Blomley and Weinmann, 2017), TLS data (Hackel et al, 2016) and MLS data (Munoz et al, 2009;Brédif et al, 2014;Weinmann, 2016). In summary, the main contributions of this paper are…”

Section: Introductionmentioning

confidence: 99%

Point Cloud Analysis for Uav-Borne Laser Scanning With Horizontally and Vertically Oriented Line Scanners – Concept and First Results

Weinmann

Müller

Hillemann

et al. 2017

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

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ABSTRACT:In this paper, we focus on UAV-borne laser scanning with the objective of densely sampling object surfaces in the local surrounding of the UAV. In this regard, using a line scanner which scans along the vertical direction and perpendicular to the flight direction results in a point cloud with low point density if the UAV moves fast. Using a line scanner which scans along the horizontal direction only delivers data corresponding to the altitude of the UAV and thus a low scene coverage. For these reasons, we present a concept and a system for UAV-borne laser scanning using multiple line scanners. Our system consists of a quadcopter equipped with horizontally and vertically oriented line scanners. We demonstrate the capabilities of our system by presenting first results obtained for a flight within an outdoor scene. Thereby, we use a downsampling of the original point cloud and different neighborhood types to extract fundamental geometric features which in turn can be used for scene interpretation with respect to linear, planar or volumetric structures.

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

Cited by 25 publications

References 34 publications

Systematic Comparison of Power Line Classification Methods from ALS and MLS Point Cloud Data

Systematic Comparison of Power Line Classification Methods from ALS and MLS Point Cloud Data

Geospatial Computer Vision Based on Multi-Modal Data—How Valuable Is Shape Information for the Extraction of Semantic Information?

Point Cloud Analysis for Uav-Borne Laser Scanning With Horizontally and Vertically Oriented Line Scanners – Concept and First Results

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