Selection of LiDAR geometric features with adaptive neighborhood size for urban land cover classification

Dong, Weihua; Lan, Jianhang; Liang, Shunlin; Yao, Wei; Zhan, Zhicheng

doi:10.1016/j.jag.2017.04.003

Cited by 28 publications

(30 citation statements)

References 23 publications

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“…Compared with airborne laser scanning, image acquisition in photogrammetry is mostly cheaper and more efficient in data acquisition flights (Hobi and Ginzler, 2012;Nurminen et al, 2013;Maltezos et al 2016). In many countries photogrammetric image blocks are captured anyway for administrative and planning purposes with decreasing time intervals, so the question is to what extent these data can be used to replace ALS data in various application domains such as Digital Terrain Model (DTM) acquisition (Ressl et al, 2016), forestry mapping (Mura et al, 2015), classification and object extraction (Tomljenovic et al, 2016;Dong et al, 2017), and 3D modeling (Xiong et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

A patch-based method for the evaluation of dense image matching quality

Zhang

Gerke²,

Vosselman

et al. 2018

International Journal of Applied Earth Observation and Geoinfor

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Airborne laser scanning and photogrammetry are two main techniques to obtain 3D data representing the object surface. Due to the high cost of laser scanning, we want to explore the potential of using point clouds derived by dense image matching (DIM), as effective alternatives to laser scanning data. We present a framework to evaluate point clouds from dense image matching and derived Digital Surface Models (DSM) based on automatically extracted sample patches. Dense matching error and noise level are evaluated quantitatively at both the local level and whole block level. Experiments show that the optimal vertical accuracy achieved by dense matching is as follows: the mean offset to the reference data is 0.1 Ground Sampling Distance (GSD); the maximum offset goes up to 1.0 GSD. When additional oblique images are used in dense matching, the mean deviation, the variation of mean deviation and the level of random noise all get improved. We also detect a bias between the point cloud and DSM from a single photogrammetric workflow. This framework also allows to reveal inhomogeneity in the distribution of the dense matching errors due to over-fitted BBA network. Meanwhile, suggestions are given on the photogrammetric quality control.

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Section: Introductionmentioning

confidence: 99%

A patch-based method for the evaluation of dense image matching quality

Zhang

Gerke²,

Vosselman

et al. 2018

International Journal of Applied Earth Observation and Geoinfor

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“…Efficient point features are the premise for the LiDAR point cloud classification; therefore, feature selection and calculation are widely discussed in the literature [4,33,34,35]. In this study, features considered to be well-suited to this particular task were adopted for the initial classification of the points, as shown in Table 1.…”

Section: Methodsmentioning

confidence: 99%

Two-Layered Graph-Cuts-Based Classification of LiDAR Data in Urban Areas

Yang

Wang

et al. 2019

Sensors

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Classifying the LiDAR (Light Detection and Ranging) point cloud in the urban environment is a challenging task. Due to the complicated structures of urban objects, it is difficult to find suitable features and classifiers to efficiently category the points. A two-layered graph-cuts-based classification framework is addressed in this study. The hierarchical framework includes a bottom layer that defines the features and classifies point clouds at the point level as well as a top layer that defines the features and classifies the point cloud at the object level. A novel adaptive local modification method is employed to model the interactions between these two layers. The iterative graph cuts algorithm runs around the bottom and top layers to optimize the classification. In this way, the addressed framework benefits from the integration of point features and object features to improve the classification. The experiments demonstrate that the proposed method is capable of producing classification results with high accuracy and efficiency.

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“…In [8], a neighborhood selection approach based on multiple individually optimized neighborhoods is proposed. Similarly, in [17], multiscale neighborhoods for selecting features are introduced to enhance the performance of 3-D point clouds classification. Although different classifiers will definitely influence the performances of the entire classification workflow, the importance of features also matters a lot, especially when the classifier is identified and has insufficient training samples.…”

Section: A Extraction Of Featuresmentioning

confidence: 99%

Classification of LiDAR Point Clouds Using Supervoxel-Based Detrended Feature and Perception-Weighted Graphical Model

Yao

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Interpretation of 3-D scene through LiDAR point clouds has been a hot research topic for decades. To utilize measured points in the scene, assigning unique tags to the points of the scene with labels linking to individual objects plays a crucial role in the analysis process. In this article, we present a supervised classification approach for the semantic labeling of laser scanning points. A novel method for extracting geometric features is proposed, removing redundant and insignificant information in the local neighborhood of the supervoxels. The proposed feature extraction method uses the supervoxel-based local neighborhood instead of points as basic elements, encapsulating the geometric features of local points. Based on the initial classification results, the graph-based optimization is used to spatially smooth the labeling results, based on the graphical model using the perception weighted edges. Benefiting from the graphbased optimization process, our supervised classification method required only a few training datasets. Experiments were carried out by comparing the semantic labeling results with manually generated ground truth datasets. The performance of the proposed methods with different characteristics was analyzed. By using our testing datasets, we have achieved an overall accuracy of better than 0.8 for assigning the measured points to eight semantic classes.

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Selection of LiDAR geometric features with adaptive neighborhood size for urban land cover classification

Cited by 28 publications

References 23 publications

A patch-based method for the evaluation of dense image matching quality

A patch-based method for the evaluation of dense image matching quality

Two-Layered Graph-Cuts-Based Classification of LiDAR Data in Urban Areas

Classification of LiDAR Point Clouds Using Supervoxel-Based Detrended Feature and Perception-Weighted Graphical Model

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