Aerial Lidar Data Classification using AdaBoost

Lodha, Suresh K.; Fitzpatrick, Darren; Helmbold, David P.

doi:10.1109/3dim.2007.10

Cited by 112 publications

(83 citation statements)

References 15 publications

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“…Thereby, the straightforward solution consists in applying standard classifiers such as a Support Vector Machine classifier, a Random Forest classifier, a Bayesian Discriminant classifier, etc. for classifying 3D points based on the derived feature vectors (Lodha et al, 2006;Lodha et al, 2007;Mallet et al, 2011;Guo et al, 2011;Khoshelham and Oude Elberink, 2012;Weinmann et al, 2015a). Respective classifiers are available in numerous software tools and easy-to-use, but the achieved labeling typically reveals a noisy appearance since no spatial correlation between class labels of neighboring 3D points is taken into account.…”

Section: Classificationmentioning

confidence: 99%

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

Blomley

Jutzi

Weinmann

2016

GEOBIA 2016: Solutions and Synergies

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ABSTRACT:The semantic labeling of 3D point clouds acquired via airborne laser scanning typically relies on the use of geometric features. In this paper, we present a framework considering complementary types of geometric features extracted from multi-scale, multi-type neighborhoods to describe (i) the local 3D structure for neighborhoods of different scale and type and (ii) how the local 3D structure behaves across different scales and across different neighborhood types. The derived features are provided as input for several classifiers with different learning principles in order to show the potential and limitations of the proposed geometric features with respect to the classification task. To allow a comparison of the performance of our framework to the performance of existing and future approaches, we evaluate our framework on the publicly available dataset provided for the ISPRS benchmark on 3D semantic labeling.

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Section: Classificationmentioning

confidence: 99%

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

Blomley

Jutzi

Weinmann

2016

GEOBIA 2016: Solutions and Synergies

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show abstract

“…In this regard, the straightforward solution consists in selecting a standard approach for supervised classification, e.g. a Support Vector Machine classifier Lodha et al, 2006), a Random Forest classifier (Chehata et al, 2009;Guo et al, 2011;Steinsiek et al, 2017), an AdaBoost(-like) classifier (Lodha et al, 2007;Guo et al, 2015) or a Bayesian Discriminant Analysis classifier (Khoshelham and Oude Elberink, 2012). However, as these classifiers treat each point of the point cloud individually, they do not take into account a spatial regularity of the derived labeling, i.e.…”

Section: Classificationmentioning

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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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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“…In our work, Random-Forest (Breiman, 2001) are chosen because they are one of the most accurate general purpose classifier and have multiple advantages compared to SVM for example, with similar results (Mountrakis et al, 2011) or Gaussian Mixture Model (Lalonde et al, 2005), Markov Random Fields (Munoz et al, 2009) and Adaboost (Lodha et al, 2007).…”

Section: Classifiermentioning

confidence: 99%

Fast and Robust Segmentation and Classification for Change Detection in Urban Point Clouds

Roynard¹,

Deschaud²,

Goulette³

2016

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

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ABSTRACT:Change detection is an important issue in city monitoring to analyse street furniture, road works, car parking, etc. For example, parking surveys are needed but are currently a laborious task involving sending operators in the streets to identify the changes in car locations. In this paper, we propose a method that performs a fast and robust segmentation and classification of urban point clouds, that can be used for change detection. We apply this method to detect the cars, as a particular object class, in order to perform parking surveys automatically. A recently proposed method already addresses the need for fast segmentation and classification of urban point clouds, using elevation images. The interest to work on images is that processing is much faster, proven and robust. However there may be a loss of information in complex 3D cases: for example when objects are one above the other, typically a car under a tree or a pedestrian under a balcony. In this paper we propose a method that retain the three-dimensional information while preserving fast computation times and improving segmentation and classification accuracy. It is based on fast region-growing using an octree, for the segmentation, and specific descriptors with Random-Forest for the classification. Experiments have been performed on large urban point clouds acquired by Mobile Laser Scanning. They show that the method is as fast as the state of the art, and that it gives more robust results in the complex 3D cases.

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Aerial Lidar Data Classification using AdaBoost

Abstract: Abstract

Cited by 112 publications

References 15 publications

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

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

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

Fast and Robust Segmentation and Classification for Change Detection in Urban Point Clouds

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