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Intuitive description of the mean shift procedure: find the densest regions in the distribution of identical billiard balls.
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Airborne LiDAR (Light Detection And Ranging) remote sensing for individual tree-level forest inventory necessitates proper extraction of individual trees and accurate measurement of tree structural parameters. Due to the inadequate tree finding capability offered by LiDAR technology and the complex patterns of forest canopies, significant omission...
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... used for clustering of unstructured data, the mean shift algorithm aims to move each data point to the densest area in its vicinity until convergence to the local maxima by iteratively performing the shift operation based on kernel density estimation. Figure 3 shows the density estimation procedure with the mean shift vector, which is the difference between the weighted mean and the center of a kernel window (i.e., the region of interest). Given n data points x j (j = 1, . . . ...
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... No caso de aplicações sobre a vegetação, o uso de dados LiDAR permite determinar as variáveis dendrométricas (MARTINS-NETO, 2016), tais como: altura da árvore, diâmetro da copa, número de indivíduos arbóreos, altura e diâmetro dos troncos. Essas métricas são de suma importância para a Engenharia Florestal, Engenharia Ambiental, Inventário Florestal e, consequentemente para o manejo de florestas e plantações (CHEN et al., 2018;DONG et al., 2020;LIU et al., 2021). Além disso, a delimitação da copa das árvores é também essencial para aprimorar algoritmos de contorno de edificações e de linhas de transmissão, uma vez que a vegetação pode causar confusão no processo de segmentação e classificação desses objetos (OLIVEIRA, GALO, 2018;OLIVEIRA, 2022) e a proximidade com as linhas de transmissão representa risco em caso de tempestades. ...
... Tradicionalmente, os trabalhos de detecção e delimitação de árvores consideram o uso de imagens bem como de Modelos de Altura da Copa (CHM -Canopy Height Model) geradas a partir dos pontos 3D (RIBAS e ELMIRO, 2013;BARBOSA, 2017). No entanto, abordagens que trabalham diretamente com os dados tridimensionais podem ser adotadas (CHEN et al., 2018;JASKIERNIAK et al., 2021;KOCH;HEYDER;WEINACKER, 2006;LU et al., 2014). Nestes casos, é notada uma melhora na taxa de detecção em regiões onde há árvores com copas complexas e distribuição não regular. ...
... A análise de nuvens de pontos LiDAR para reconhecimento de objetos também é assunto de estudos por parte da visão computacional, onde se faz o uso de descritores geométricos 3D para discriminar os objetos com base nas suas características geométricas (CHEN et al., 2018;WEINMANN et al., 2015WEINMANN et al., , 2017. No trabalho apresentado por (WEINMANN et al., 2015), por exemplo, foi proposta uma metodologia que consistiu em selecionar a vizinhança definida no entorno de cada ponto da nuvem, determinar o conjunto de atributos extraídos com base nos autovalores calculados para cada vizinhança e depois realizar uma classificação supervisionada dos pontos entre as classes linha de transmissão, poste/tronco, fachada, terreno e vegetação. ...
The result derived from tree detection using LiDAR data can be used in different applications such as forest management and preservation, urban planning, detection of occluded objects by tree crowns, among others. In this sense, this work aims to evaluate the applicability of 3D geometric descriptors based on eigenvalues and LiDAR intensity in tree detection. In experiments, it was analyzed the influence of neighborhood (sphere and cylinder) in the calculation of geometric attributes. From visual analyses, it was possible to notice that the use of some geometric attributes such as omnivariance, curvature, planarity and eigenentropy showed more potential in detecting trees. Considering the four selected attributes (omnivariance, curvature, planarity, eigenentropy) and intensity, the K-Means algorithm was executed to each attribute aiming to separate the point cloud into tree and non-tree. The results derived from classification were compared with reference data using quality parameters such as completeness, correctness and F-Score. Completeness values obtained with for omnivariance, eigenentropy, planarity and intensity reached values above 90%, indicating that these descriptors have high reliability for tree detection. The average correctness was around 62%, presenting a large number of false positives. When analyzing F-Score values, it was possible to verify the potential of omnivariance, computed in a spherical neighborhood, in detecting trees from LiDAR data (F-Score above 80%).
... MeanShift [20,22,31] is a classical mode-seeking clustering algorithm that has a wide range of applications across machine learning and computer vision. Recent applications within computer vision include object tracking [54,78,47], unsupervised image segmentation [67,13,97], video segmentation [56,24,55], image restoration [4,9], edge-preserving smoothing [55,8,12], point clouds [46,80,96], and remote sensing [51,61,18,50]. More broadly in machine learning, MeanShift has been used for semi-supervised clustering [2,73], manifold denoising [86,82], matrix completion [83,21], anomaly detection [6,94,70], as well as numerous problems in medical imaging [7,69,98,49,68,53,99,101], wireless sensor networks [95,100,89,64,85,62], and robotics [44,45,36,43,92,16]. ...
MeanShift is a popular mode-seeking clustering algorithm used in a wide range of applications in machine learning. However, it is known to be prohibitively slow, with quadratic runtime per iteration. We propose MeanShift++, an extremely fast mode-seeking algorithm based on MeanShift that uses a grid-based approach to speed up the mean shift step, replacing the computationally expensive neighbors search with a density-weighted mean of adjacent grid cells. In addition, we show that this grid-based technique for density estimation comes with theoretical guarantees. The runtime is linear in the number of points and exponential in dimension, which makes MeanShift++ ideal on low-dimensional applications such as image segmentation and object tracking. We provide extensive experimental analysis showing that MeanShift++ can be more than 10,000x faster than MeanShift with competitive clustering results on benchmark datasets and nearly identical image segmentations as MeanShift. Finally, we show promising results for object tracking.
... Generally, the bandwidth parameter has a great influence on the results of individual tree segmentation in the Mean shift method. Chen et al. [40] first extracted the trunk and then estimated the bandwidth with the spatial location information of the trunk to obtain accurate bandwidth parameters. ...
Individual tree extraction is an important process for forest resource surveying and monitoring. To obtain more accurate individual tree extraction results, this paper proposed an individual tree extraction method based on transfer learning and Gaussian mixture model separation. In this study, transfer learning is first adopted in classifying trunk points, which can be used as clustering centers for tree initial segmentation. Subsequently, principal component analysis (PCA) transformation and kernel density estimation are proposed to determine the number of mixed components in the initial segmentation. Based on the number of mixed components, the Gaussian mixture model separation is proposed to separate canopies for each individual tree. Finally, the trunk stems corresponding to each canopy are extracted based on the vertical continuity principle. Six tree plots with different forest environments were used to test the performance of the proposed method. Experimental results show that the proposed method can achieve 87.68% average correctness, which is much higher than that of other two classical methods. In terms of completeness and mean accuracy, the proposed method also outperforms the other two methods.
... Compared with other clustering methods, mean shift does not require seed points or number of clusters before clustering. It proves to be robust to segment various kinds of trees [34,35]. Mean shift aims to move each data point to the densest area within a certain neighborhood by iteratively performing shift operations based on a kernel density function. ...
Unmanned aerial vehicles using light detection and ranging (UAV LiDAR) with high spatial resolution have shown great potential in forest applications because they can capture vertical structures of forests. Individual tree segmentation is the foundation of many forest research works and applications. The tradition fixed bandwidth mean shift has been applied to individual tree segmentation and proved to be robust in tree segmentation. However, the fixed bandwidth-based segmentation methods are not suitable for various crown sizes, resulting in omission or commission errors. Therefore, to increase tree-segmentation accuracy, we propose a self-adaptive bandwidth estimation method to estimate the optimal kernel bandwidth automatically without any prior knowledge of crown size. First, from the global maximum point, we divide the three-dimensional (3D) space into a set of angular sectors, for each of which a canopy surface is simulated and the potential tree crown boundaries are identified to estimate average crown width as the kernel bandwidth. Afterwards, we use a mean shift with the automatically estimated kernel bandwidth to extract individual tree points. The method is iteratively implemented within a given area until all trees are segmented. The proposed method was tested on the 7 plots acquired by a Velodyne 16E LiDAR system, including 3 simple plots and 4 complex plots, and 95% and 80% of trees were correctly segmented, respectively. Comparative experiments show that our method contributes to the improvement of both segmentation accuracy and computational efficiency.
... From the computational viewpoint, these methods can be classified into volumetric and profiler methods. Volumetric-based methods directly search the 3D volume for delineating individual trees [10,19,20,[27][28][29][30][31]; hence, they are generally highly computationally demanding and so the methods have focused on small regions within a single study site and may not be applicable across a wide range of forest types [9]. On the other hand, the profiler methods reduce the computational load through a more modular process. ...
... The highest point among the point cloud of each segmented tree is considered as the tree apex, and the distance from the tree apex to the ground is regarded as the height of that tree. Since the performance of trunk detection depends on the lidar point density and the forest structure, not all tree trunks can be correctly extracted [27]; the apex of the LiDAR-derived tree is considered as its location in this paper. Figure 10a,b show the comparison between the original and segmented point clouds for one of the 271 plots. ...
... and the distance from the tree apex to the ground is regarded as the height of that tree. Since the performance of trunk detection depends on the lidar point density and the forest structure, not all tree trunks can be correctly extracted [27]; the apex of the LiDAR-derived tree is considered as its location in this paper. Figure 10a,b show the comparison between the original and segmented point clouds for one of the 271 plots. ...
The development of new approaches to tree-level parameter extraction for forest resource inventory and management is an important area of ongoing research, which puts forward high requirements for the capabilities of single-tree segmentation and detection methods. Conventional methods implement segmenting routine with same resolution threshold for overstory and understory, ignoring that their lidar point densities are different, which leads to over-segmentation of the understory trees. To improve the segmentation accuracy of understory trees, this paper presents a multi-threshold segmentation approach for tree-level parameter extraction using small-footprint airborne LiDAR (Light Detection And Ranging) data. First, the point clouds are pre-processed and encoded to canopy layers according to the lidar return number, and multi-threshold segmentation using DSM-based (Digital Surface Model) method is implemented for each layer; tree segments are then combined across layers by merging criteria. Finally, individual trees are delineated, and tree parameters are extracted. The novelty of this method lies in its application of multi-resolution threshold segmentation strategy according to the variation of LiDAR point density in different canopy layers. We applied this approach to 271 permanent sample plots of the University of Kentucky’s Robinson Forest, a deciduous canopy-closed forest with complex terrain and vegetation conditions. Experimental results show that a combination of multi-resolution threshold segmentation based on stratification and cross-layer tree segments merging method can provide a significant performance improvement in individual tree-level forest measurement. Compared with DSM-based method, the proposed multi-threshold segmentation approach strongly improved the average detection rate (from 52.3% to 73.4%) and average overall accuracy (from 65.2% to 76.9%) for understory trees. The overall accuracy increased from 75.1% to 82.6% for all trees, with an increase of the coefficient of determination R2 by 20 percentage points. The improvement of tree detection method brings the estimation of structural parameters for single trees up to an accuracy level: For tree height, R2 increased by 5.0 percentage points from 90% to 95%; and for tree location, the mean difference decreased by 23 cm from 105 cm to 82 cm.
... This has also been observed by Xu et al. (2018) in a related study which has used a different approach of minimum distance rule for combing different supervoxels belonging to a single tree canopy. The observed high success rate (99%) in detecting most of the tree canopies, which may vary by canopy shape, tree height and neighbourhood, in this study supports results of Chen et al. (2018); Wu et al. (2018) which suggest that 3D segmentation of LiDAR point cloud can be a general approach for detection and modelling of trees and other urban objects at individual entity level. Moreover, many of the interlocked trees which were counted as a single tree in the reference image using very high resolution images are clearly separated in the proposed approach using LiDAR point cloud. ...
Precise mapping of urban green spaces is critical for sustainable development of urban ecosystem. LiDAR remote sensing technology has been proved to be valuable for capturing geometrical structure of natural and man-made resources. However, there has been no automatic and operational method to extract individual urban trees. This is largely due to the complexity involved in processing the massive LiDAR point cloud. It has been a challenge to label tree canopy point cloud compared to the point cloud from man-made structures such as buildings. This study proposes an object-based labeling framework for delineating individual tree canopy clusters in urban green spaces from airborne LiDAR point cloud. In addition, to reduce the computational complexity, supervoxels - a computer vision technique, has been applied as a pre-labeling process. The LiDAR point cloud is then semantically labeled using an object-based point cloud labeling framework. Experiments on an airborne LiDAR point cloud of a public park in Bergschen-hoek, Netherlands demonstrate the proposed methodology offers 99% accuracy when compared with the reference individual tree canopies data. The results also indicate the possibility of delineating multiple tree canopies which are overlapped, a situation which often leads to erroneous results from optical imagery. The proposed methodology flow results in a vector shape file as the output with individual tree locations categorized based on elevation.
... A spherical kernel was chosen instead of a cylinder-shaped kernel, as in previous studies. Both the segmentation and localization results were improved by detecting the tree trunks first, in order to complement the adaptive mean shift segmentation [44]. ...
Airborne lidar has been widely used for forest characterization to facilitate forest ecological and management studies. With the availability of increasingly higher point density, individual tree delineation (ITD) from airborne lidar point clouds has become a popular yet challenging topic, due to the complexity and diversity of forests. One important step of ITD is segmentation, for which various methodologies have been studied. Among them, a long proven image segmentation method, mean shift, has been applied directly onto 3D points, and has shown promising results. However, there are variations among those who implemented the algorithm in terms of the kernel shape, adaptiveness and weighting. This paper provides a detailed assessment of the mean shift algorithm for the segmentation of airborne lidar data, and the effect of crown top detection upon the validation of segmentation results. The results from three different datasets revealed that a crown-shaped kernel consistently generates better results (up to 7 percent) than other variants, whereas weighting and adaptiveness do not warrant improvements.
... However, there appeared many recent researches related to detecting individual trees and forest patch delineations from airborne laser scanning (ALS) point clouds based on [21], [22], [23], [24]. Aimed at error reduction and accuracy refinement, the research [25] presents an adaptive mean shiftbased clustering scheme aided by a tree trunk detection technique to segment individual trees and estimate tree structural parameters based solely on the airborne LiDAR data. [26] developed an algorithm to segment individual trees from the small footprint discrete return airborne LiDAR point cloud. ...
The paper presents the study and implementation of the ground detection methodology with filtration and removal of forest points from LiDAR-based 3D point cloud using the Cloth Simulation Filtering (CSF) algorithm. The methodology allows to recover a terrestrial relief and create a landscape map of a forestry region. As the proof-of-concept, we provided the outdoor flight experiment, launching a hexacopter under a mixed forestry region with sharp ground changes nearby Innopolis city (Russia), which demonstrated the encouraging results for both ground detection and methodology robustness.
... However, there appeared many recent researches related to detecting individual trees and forest patch delineations from airborne laser scanning (ALS) point clouds based on [21], [22], [23], [24]. Aimed at error reduction and accuracy refinement, the research [25] presents an adaptive mean shiftbased clustering scheme aided by a tree trunk detection technique to segment individual trees and estimate tree structural parameters based solely on the airborne LiDAR data. [26] developed an algorithm to segment individual trees from the small footprint discrete return airborne LiDAR point cloud. ...
The paper presents the study and implementation of the ground detection methodology with filtration and removal of forest points from LiDAR-based 3D point cloud using the Cloth Simulation Filtering (CSF) algorithm. The methodology allows to recover a terrestrial relief and create a landscape map of a forestry region. As the proof-of-concept, we provided the outdoor flight experiment, launching a hexacopter under a mixed forestry region with sharp ground changes nearby Innopolis city (Russia), which demonstrated the encouraging results for both ground detection and methodology robustness.
... However, Barnes et al. [84] indicated that watershed algorithms performed significantly better than the region-growing algorithm, Jaskierniak et al. [86] prefer local maximum filtering over normalized cut algorithm. Comparison of five selected algorithms utilizing canopy data [95] favors adaptive mean shift algorithm with precision of 91%. ...
... An alternative approach is Trunk Detection-Aided Mean Shift Clustering Technique [95], where positions of trunks detected from vertical histograms serve as references for delineation of individual trees. Using this approach, authors were able to correctly detect more than 91% of trees with a positional accuracy significantly improved (positioning error reduced by 33%) compared to CHM based algorithms. ...
In recent decades, remote sensing techniques and the associated hardware and software have made substantial improvements. With satellite images that can obtain sub-meter spatial resolution, and new hardware, particularly unmanned aerial vehicles and systems, there are many emerging opportunities for improved data acquisition, including variable temporal and spectral resolutions. Combined with the evolution of techniques for aerial remote sensing, such as full wave laser scanners, hyperspectral scanners, and aerial radar sensors, the potential to incorporate this new data in forest management is enormous. Here we provide an overview of the current state-of-the-art remote sensing techniques for large forest areas thousands or tens of thousands of hectares. We examined modern remote sensing techniques used to obtain forest data that are directly applicable to decision making issues, and we provided a general overview of the types of data that can be obtained using remote sensing. The most easily accessible forest variable described in many works is stand or tree height, followed by other inventory variables like basal area, tree number, diameters, and volume, which are crucial in decision making process, especially for thinning and harvest planning, and timber transport optimization. Information about zonation and species composition are often described as more difficult to assess; however, this information usually is not required on annual basis. Counts of studies on forest health show an increasing trend in the last years, mostly in context of availability of new sensors as well as increased forest vulnerability caused by climate change; by virtue to modern sensors interesting methods were developed for detection of stressed or damaged trees. Unexpectedly few works focus on regeneration and seedlings evaluation; though regenerated stands should be regularly monitored in order to maintain forest cover sustainability.
