Classification of lidar bare-earth points, buildings, vegetation, and small objects based on region growing and angular classifier

Sánchez-Lopera, José; Lerma, José Luis

doi:10.1080/01431161.2014.960619

Cited by 16 publications

(4 citation statements)

References 31 publications

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“…Due to the fact that buildings occupy a specific area and the façade of buildings act as barriers to prevent ground points falling inside the building area, the maximum intersection angle of real building points is larger than 90 • at the building corners, and larger than 180 • away from building corner. While, the maximum intersection angle of vegetation points or other non-building points is less than 90 • due to the fact that it is surrounded by ground and non-building points in all directions within a cylinder (yellow circle in Figure 5c) [58].…”

Section: Consistency Constraintmentioning

confidence: 99%

“…The maximum intersection angle constraint takes into account two threshold parameters: radius of cylinder to detect neighborhoods from ground and non-building points and angular threshold to consider the minimum angle defined by the façade alignments at the corners. In the proposed method, the radius is empirically set to 2.5 m according to [12,37] and the angular threshold was set to 90 • according to [58].…”

Section: Consistency Constraintmentioning

confidence: 99%

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Building Extraction from Airborne LiDAR Data Based on Min-Cut and Improved Post-Processing

Liu

et al. 2020

Remote Sensing

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Building extraction from LiDAR data has been an active research area, but it is difficult to discriminate between buildings and vegetation in complex urban scenes. A building extraction method from LiDAR data based on minimum cut (min-cut) and improved post-processing is proposed. To discriminate building points on the intersecting roof planes from vegetation, a point feature based on the variance of normal vectors estimated via low-rank subspace clustering (LRSC) technique is proposed, and non-ground points are separated into two subsets based on min-cut after filtering. Then, the results of building extraction are refined via improved post-processing using restricted region growing and the constraints of height, the maximum intersection angle and consistency. The maximum intersection angle constraint removes large non-building point clusters with narrow width, such as greenbelt along streets. Contextual information and consistency constraint are both used to eliminate inhomogeneity. Experiments of seven datasets, including five datasets provided by the International Society for Photogrammetry and Remote Sensing (ISPRS), one dataset with high-density point data and one dataset with dense buildings, verify that most buildings, even with curved roofs, are successfully extracted by the proposed method, with over 94.1% completeness and a minimum 89.8% correctness at the per-area level. In addition, the proposed point feature significantly outperforms the comparison alternative and is less sensitive to feature threshold in complex scenes. Hence, the extracted building points can be used in various applications.

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Section: Consistency Constraintmentioning

confidence: 99%

Section: Consistency Constraintmentioning

confidence: 99%

Building Extraction from Airborne LiDAR Data Based on Min-Cut and Improved Post-Processing

Liu

et al. 2020

Remote Sensing

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“…Pairwise CRF model is widely used in semantic classification [13,36,37] to model the spatial interaction in both the labels and observed values, which is of importance in semantic classification. It is a discriminative classification approach, which directly models the posterior probability of the label y conditioned on the observed data x [38,39]. No more than two kinds of cliques are defined in a pairwise CRF.…”

Section: Pairwise Crf Modelmentioning

confidence: 99%

Large-Scale ALS Data Semantic Classification Integrating Location-Context-Semantics Cues by Higher-Order CRF

Han

Wang

Huang

et al. 2020

Sensors

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We designed a location-context-semantics-based conditional random field (LCS-CRF) framework for the semantic classification of airborne laser scanning (ALS) point clouds. For ALS datasets of high spatial resolution but with severe noise pollutions, more contexture and semantics cues, besides location information, can be exploited to surmount the decrease of discrimination of features for classification. This paper mainly focuses on the semantic classification of ALS data using mixed location-context-semantics cues, which are integrated into a higher-order CRF framework by modeling the probabilistic potentials. The location cues modeled by the unary potentials can provide basic information for discriminating the various classes. The pairwise potentials consider the spatial contextual information by establishing the neighboring interactions between points to favor spatial smoothing. The semantics cues are explicitly encoded in the higher-order potentials. The higher-order potential operates at the clusters level with similar geometric and radiometric properties, guaranteeing the classification accuracy based on semantic rules. To demonstrate the performance of our approach, two standard benchmark datasets were utilized. Experiments show that our method achieves superior classification results with an overall accuracy of 83.1% on the Vaihingen Dataset and an overall accuracy of 94.3% on the Graphics and Media Lab (GML) Dataset A compared with other classification algorithms in the literature. 2 of 28 from different equipment. Therefore, we incorporate location, spatial contextual, and semantics cues within a higher-order conditional random field (CRF) framework to provide complementary information from varying perspectives, so that it can address the common misjudgment of semantic classes in ALS point clouds, from the perspectives of the accuracy of each class and the overall accuracy. Related Works for ALS Point Cloud ClassificationAccording to the type of entity used for classification, existing methods can be categorized as point-based and cluster-based (or segment-based) [2,3]. Point-based methods classify each point of the ALS data by using features as the inputs for supervised or unsupervised classifiers [4], while cluster-based methods segment the ALS data into clusters, then class labels are assigned to the clusters in which all points share the same class label [5,6]. We briefly review the aforementioned methods, and demonstrate the rationale for our method in what follows.Point-based methods generally extract point-wise features locally from the neighborhood defined by a sphere or cylinder. Therefore, such methods usually focus on the selection of discriminative features and effective classifiers. For instance, Reference [7] worked on 3D scene analysis, including geometric features extraction and optimal neighbors selection. Then an optimal eigenentropy-based scale selection method was proposed. Reference [2] combined airborne LiDAR with images to extract more discriminative features. Then, based on these ...

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“…However, it is time consuming in selecting samples, and the result is highly dependent on samples [14]. Segmentation-based methods begin by splitting point clouds into disjointed segments, and then extract building segments with some prior knowledge or assumptions [16,[24][25][26][27]. Generally, segmentation-based methods are widely utilized in various engineering applications.…”

Section: Introductionmentioning

confidence: 99%

Automated Reconstruction of Building LoDs from Airborne LiDAR Point Clouds Using an Improved Morphological Scale Space

Wang

Huang

et al. 2016

Remote Sensing

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Abstract:Reconstructing building models at different levels of detail (LoDs) from airborne laser scanning point clouds is urgently needed for wide application as this method can balance between the user's requirements and economic costs. The previous methods reconstruct building LoDs from the finest 3D building models rather than from point clouds, resulting in heavy costs and inflexible adaptivity. The scale space is a sound theory for multi-scale representation of an object from a coarser level to a finer level. Therefore, this paper proposes a novel method to reconstruct buildings at different LoDs from airborne Light Detection and Ranging (LiDAR) point clouds based on an improved morphological scale space. The proposed method first extracts building candidate regions following the separation of ground and non-ground points. For each building candidate region, the proposed method generates a scale space by iteratively using the improved morphological reconstruction with the increase of scale, and constructs the corresponding topological relationship graphs (TRGs) across scales. Secondly, the proposed method robustly extracts building points by using features based on the TRG. Finally, the proposed method reconstructs each building at different LoDs according to the TRG. The experiments demonstrate that the proposed method robustly extracts the buildings with details (e.g., door eaves and roof furniture) and illustrate good performance in distinguishing buildings from vegetation or other objects, while automatically reconstructing building LoDs from the finest building points.

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Classification of lidar bare-earth points, buildings, vegetation, and small objects based on region growing and angular classifier

Cited by 16 publications

References 31 publications

Building Extraction from Airborne LiDAR Data Based on Min-Cut and Improved Post-Processing

Building Extraction from Airborne LiDAR Data Based on Min-Cut and Improved Post-Processing

Large-Scale ALS Data Semantic Classification Integrating Location-Context-Semantics Cues by Higher-Order CRF

Automated Reconstruction of Building LoDs from Airborne LiDAR Point Clouds Using an Improved Morphological Scale Space

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