Improving LiDAR classification accuracy by contextual label smoothing in post-processing

Li, Nan; Liu, Chun; Pfeifer, Norbert

doi:10.1016/j.isprsjprs.2018.11.022

Cited by 26 publications

(20 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We solve this minimization problem using the same strategy applied by Li, Liu, and Pfeifer (), which was originally proposed in by Landrieu et al (). The fidelity term

normalΦ

is a linear one

ϕ_{linear} false(S, S^{'} false) ≐ - false⟨ S, S^{'} false⟩ = - \underset{k \in C}{false\sum} S_{k} S_{k}^{'} .

The regularizer

normalΨ

is structured by the adjacency graph G = ( V , E ).…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

LeWoS: A universal leaf‐wood classification method to facilitate the 3D modelling of large tropical trees using terrestrial LiDAR

2020

View full text Add to dashboard Cite

Leaf‐wood separation in terrestrial LiDAR data is a prerequisite for non‐destructively estimating biophysical forest properties such as standing wood volumes and leaf area distributions. Current methods have not been extensively applied and tested on tropical trees. Moreover, their impacts on the accuracy of subsequent wood volume retrieval were rarely explored. We present LeWoS, a new fully automatic tool to automate the separation of leaf and wood components, based only on geometric information at both the plot and individual tree scales. This data‐driven method utilizes recursive point cloud segmentation and regularization procedures. Only one parameter is required, which makes our method easily and universally applicable to data from any LiDAR technology and forest type. We conducted a twofold evaluation of the LeWoS method on an extensive dataset of 61 tropical trees. We first assessed the point‐wise classification accuracy, yielding a score of 0.91 ± 0.03 in average. Second, we evaluated the impact of the proposed method on 3D tree models by cross‐comparing estimates in wood volume and branch length with those based on manually separated wood points. This comparison showed similar results, with relative biases of less than 9% and 21% on volume and length respectively. LeWoS allows an automated processing chain for non‐destructive tree volume and biomass estimation when coupled with 3D modelling methods. The average processing time on a laptop was 90s for 1 million points. We provide LeWoS as an open‐source tool with an end‐user interface, together with a large dataset of labelled 3D point clouds from contrasting forest structures. This study closes the gap for stand volume modelling in tropical forests where leaf and wood separation remain a crucial challenge.

show abstract

“…We solve this minimization problem using the same strategy applied by Li, Liu, and Pfeifer (), which was originally proposed in by Landrieu et al (). The fidelity term

normalΦ

is a linear one

ϕ_{linear} false(S, S^{'} false) ≐ - false⟨ S, S^{'} false⟩ = - \underset{k \in C}{false\sum} S_{k} S_{k}^{'} .

The regularizer

normalΨ

is structured by the adjacency graph G = ( V , E ).…”

Section: Methodsmentioning

confidence: 99%

“…We solve this minimization problem using the same strategy applied by Li, Liu, and Pfeifer (2019), which was originally proposed in by Landrieu et al (2017). The fidelity term Φ is a linear one…”

Section: Class Regularizationmentioning

confidence: 99%

LeWoS: A universal leaf‐wood classification method to facilitate the 3D modelling of large tropical trees using terrestrial LiDAR

2020

View full text Add to dashboard Cite

show abstract

“…Related methods of point cloud classification are categorized to segment-based classification methods [30][31][32] (closely related to the proposed classification method) and primitive-based methods [33][34][35][36][37][38][39][40][41][42]. The primitive-based methods are divided into two categories: point-based methods [33][34][35][36][37][38] and voxel-based methods or super-voxel based methods [39][40][41][42]. These primitive-based methods would also provide a clue for researchers who are interested in the classification of pole-like street furniture.…”

Section: Point Cloud Classificationmentioning

confidence: 99%

“…The point-based methods concentrate on point-level features, and attempt to classify each point to a specified class. Since the point-level features may find it difficult to preserve the smooth labeling for different objects, many researchers have tried to solve these problems by using an optimal scale to acquire more accurate geometric features [33,36] or combining contextual information [35,38]. Brodu et al improved natural scene classification results by utilizing a multi-scale dimensionality criterion [34] while Weinmann et al enhanced their classification results by experimenting with different neighbor sizes to find the optimal neighborhood [36].…”

Section: Point Cloud Classificationmentioning

confidence: 99%

“…Li also incorporated contextual information to their classification method [38]. They enhanced the label smoothing in two ways: each point can collect sufficient neighborhood information to achieve an optimal graph; they improved the input label probability set by probabilistic label relaxation to make the labels more consistent with spatial context.…”

Section: Point Cloud Classificationmentioning

confidence: 99%

See 1 more Smart Citation

Pole-Like Street Furniture Segmentation and Classification in Mobile LiDAR Data by Integrating Multiple Shape-Descriptor Constraints

Wang

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

Nowadays, mobile laser scanning is widely used for understanding urban scenes, especially for extraction and recognition of pole-like street furniture, such as lampposts, traffic lights and traffic signs. However, the start-of-art methods may generate low segmentation accuracy in the overlapping scenes, and the object classification accuracy can be highly influenced by the large discrepancy in instance number of different objects in the same scene. To address these issues, we present a complete paradigm for pole-like street furniture segmentation and classification using mobile LiDAR (light detection and ranging) point cloud. First, we propose a 3D density-based segmentation algorithm which considers two different conditions including isolated furniture and connected furniture in overlapping scenes. After that, a vertical region grow algorithm is employed for component splitting and a new shape distribution estimation method is proposed to obtain more accurate global shape descriptors. For object classification, an integrated shape constraint based on the splitting result of pole-like street furniture (SplitISC) is introduced and integrated into a retrieval procedure. Two test datasets are used to verify the performance and effectiveness of the proposed method. The experimental results demonstrate that the proposed method can achieve better classification results from both sites than the existing shape distribution method.

show abstract

Identification of geomorphological hazards in an underground coal mining area based on an improved region merging watershed algorithm

Zhang

Xue

et al. 2020

Arab J Geosci

View full text Add to dashboard Cite

Improving LiDAR classification accuracy by contextual label smoothing in post-processing

Cited by 26 publications

References 37 publications

LeWoS: A universal leaf‐wood classification method to facilitate the 3D modelling of large tropical trees using terrestrial LiDAR

LeWoS: A universal leaf‐wood classification method to facilitate the 3D modelling of large tropical trees using terrestrial LiDAR

Pole-Like Street Furniture Segmentation and Classification in Mobile LiDAR Data by Integrating Multiple Shape-Descriptor Constraints

Identification of geomorphological hazards in an underground coal mining area based on an improved region merging watershed algorithm

Contact Info

Product

Resources

About