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Intuitive description of the mean shift procedure: find the densest regions in the distribution of identical billiard balls. 

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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... 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]. ...
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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. ...
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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. ...
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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. ...
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... 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. ...
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... 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. ...
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... 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. ...
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... 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. ...
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... In communes with more uniform proportion between the afforested parcels and the parcels that experienced succession, the two cases should be distinguished first. A possible solution to that problem is the careful analysis of the vegetation structure, e.g., using LiDAR point clouds [35,36]. ...
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One of the major land use and land cover changes in Europe is agricultural land abandonment (ALA) that particularly affects marginal mountain areas. Accurate mapping of ALA patterns and timing is important for understanding its determinants and the environmental and socioeconomic consequences. In highly fragmented agricultural landscapes with small-scale farming, subtle land use changes following ALA can be detected with high resolution remotely sensed data, and successional vegetation height is a possible indicator of ALA timing. The main aim of this study was to determine the relationship between successional vegetation height and the timing of agricultural land abandonment in the Budzów community in the Polish Carpathians. Areas of vegetation succession were vectorized on 1977, 1997, and 2009 orthophotomaps, enabling the distinguishing of vegetation encroaching on abandoned fields before and after 1997. Vegetation height in 2012-2014 was determined from digital surface and terrain models that were derived from airborne laser scanning data. The median heights of successional vegetation that started development before and after 1997 were different (6.9 m and 3.2 m, respectively). No significant correlations between successional vegetation height and elevation, slope, aspect, and proximity to forest were found. Thus, the timing of agricultural land abandonment is the most important factor influencing vegetation height, whereas environmental characteristics on this scale of investigation may be neglected.