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Nowadays, laser scanning technology has provided an effective and non-destructive approach to reveal the forests developmental process and physiological properties. For the purpose of obtaining the 3D spatial structure and skeleton of trees, this work addresses the separation of wood and foliage from the forest using two phases. The first global phase develops a pointwise supervised learning framework to classify forest point clouds. In order to improve the classification accuracy, we design new features for the learning process, which supplements the current geometric features in terms of the topological information. The second local phase designs a new least-cost path model to further separate wood and foliage points. The separation of branch points is formulated as an energy function and optimized by the dynamic programming technique. Experiments on different plots show that points from stems and branches are detected as wood points completely and correctly. The achieved average completeness, correctness, and F1 score of the wood and foliage separation is 91.25%, 90.34% and 0.91 respectively, which is promising to the phenotyping study related to the organisms physical form and structure
Sheng Xu; Kai Zhou; Yuan Sun; Ting Yun. Separation of Wood and Foliage for Trees from Ground Point Clouds using a Novel Least-cost Path Model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2021, PP, 1 -1.
AMA StyleSheng Xu, Kai Zhou, Yuan Sun, Ting Yun. Separation of Wood and Foliage for Trees from Ground Point Clouds using a Novel Least-cost Path Model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021; PP (99):1-1.
Chicago/Turabian StyleSheng Xu; Kai Zhou; Yuan Sun; Ting Yun. 2021. "Separation of Wood and Foliage for Trees from Ground Point Clouds using a Novel Least-cost Path Model." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing PP, no. 99: 1-1.
Accurate individual tree crown (ITC) segmentation from scanned point clouds is a fundamental task in forest biomass monitoring and forest ecology management. Light detection and ranging (LiDAR) as a mainstream tool for forest survey is advancing the pattern of forest data acquisition. In this study, we performed a novel deep learning framework directly processing the forest point clouds belonging to the four forest types (i.e., the nursery base, the monastery garden, the mixed forest, and the defoliated forest) to realize the ITC segmentation. The specific steps of our approach were as follows: first, a voxelization strategy was conducted to subdivide the collected point clouds with various tree species from various forest types into many voxels. These voxels containing point clouds were taken as training samples for the PointNet deep learning framework to identify the tree crowns at the voxel scale. Second, based on the initial segmentation results, we used the height-related gradient information to accurately depict the boundaries of each tree crown. Meanwhile, the retrieved tree crown breadths of individual trees were compared with field measurements to verify the effectiveness of our approach. Among the four forest types, our results revealed the best performance for the nursery base (tree crown detection rate r = 0.90; crown breadth estimation R2 > 0.94 and root mean squared error (RMSE) < 0.2m). A sound performance was also achieved for the monastery garden and mixed forest, which had complex forest structures, complicated intersections of branches and different building types, with r = 0.85, R2 > 0.88 and RMSE < 0.6 m for the monastery garden and r = 0.80, R2 > 0.85 and RMSE < 0.8 m for the mixed forest. For the fourth forest plot type with the distribution of crown defoliation across the woodland, we achieved the performance with r = 0.82, R2 > 0.79 and RMSE < 0.7 m. Our method presents a robust framework inspired by the deep learning technology and computer graphics theory that solves the ITC segmentation problem and retrieves forest parameters under various forest conditions.
Xinxin Chen; Kang Jiang; Yushi Zhu; Xiangjun Wang; Ting Yun. Individual Tree Crown Segmentation Directly from UAV-Borne LiDAR Data Using the PointNet of Deep Learning. Forests 2021, 12, 131 .
AMA StyleXinxin Chen, Kang Jiang, Yushi Zhu, Xiangjun Wang, Ting Yun. Individual Tree Crown Segmentation Directly from UAV-Borne LiDAR Data Using the PointNet of Deep Learning. Forests. 2021; 12 (2):131.
Chicago/Turabian StyleXinxin Chen; Kang Jiang; Yushi Zhu; Xiangjun Wang; Ting Yun. 2021. "Individual Tree Crown Segmentation Directly from UAV-Borne LiDAR Data Using the PointNet of Deep Learning." Forests 12, no. 2: 131.
Accurately estimating and mapping forest structural parameters are essential for monitoring forest resources and understanding ecological processes. The novel deep learning algorithm has the potential to be a promising approach to improve the estimation accuracy while combining with advanced remote sensing technology. Airborne light detection and ranging (LiDAR) has the preferable capability to characterize 3-D canopy structure and estimate forest structural parameters. In this study, we developed a deep learning-based algorithm (Deep-RBN) that combined the fully connected network (FCN) deep learning algorithm with the optimized radial basis neural network (RBN) algorithm for forest structural parameter estimation using airborne LiDAR data. The multiple iterations were used to constantly update the internal weights to achieve the optimized accuracy of model fitting, and the optimized RBN algorithm was developed for the limited training sets. We assessed the efficiency and capability of the Deep-RBN in the estimation of forest structural parameters in a subtropical planted forest of southern China, by comparing the traditional FCN algorithm and multiple linear regression. We found that Deep-RBN had the strongest capability in estimates of forest structural parameters (R 2 = 0.67–0.86, rRMSE = 6.95%–20.34%). The sensitivity analysis of the key hyperparameters of Deep-RBN algorithm showed that the learning rate is one of the most important parameters that influence the performance of predictive models, and while its value equal is to 0.001, the predictive models had the highest accuracy (mean DBH: RMSE = 1.01, mean height: RMSE = 1.45, volume: RMSE = 26.49, stem density: RMSE = 121.06). With the increase of training samples added in Deep-RBN model, the predictive models performed better; however, no significant improvements of accuracy were observed while the number of training set is larger than 80. This study demonstrates the benefits of jointly using the Deep-RBN algorithm and airborne LiDAR data to improve the accuracy of forest structural parameter estimation and mapping, which provides a promising methodology for sustainable forest resources monitoring.
Hao Liu; Xin Shen; Lin Cao; Ting Yun; Zhengnan Zhang; Xiaoyao Fu; Xinxin Chen; Fangzhou Liu. Deep Learning in Forest Structural Parameter Estimation Using Airborne LiDAR Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 14, 1603 -1618.
AMA StyleHao Liu, Xin Shen, Lin Cao, Ting Yun, Zhengnan Zhang, Xiaoyao Fu, Xinxin Chen, Fangzhou Liu. Deep Learning in Forest Structural Parameter Estimation Using Airborne LiDAR Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 14 ():1603-1618.
Chicago/Turabian StyleHao Liu; Xin Shen; Lin Cao; Ting Yun; Zhengnan Zhang; Xiaoyao Fu; Xinxin Chen; Fangzhou Liu. 2020. "Deep Learning in Forest Structural Parameter Estimation Using Airborne LiDAR Data." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14, no. : 1603-1618.
The terrestrial laser scanner (TLS) has been widely used in forest inventories. However, with increasing precision of TLS, storing and transmitting tree point clouds become more challenging. In this paper, a novel compressed sensing (CS) scheme for broad-leaved tree point clouds is proposed by analyzing and comparing different sparse bases, observation matrices, and reconstruction algorithms. Our scheme starts by eliminating outliers and simplifying point clouds with statistical filtering and voxel filtering. The scheme then applies Haar sparse basis to thin the coordinate data based on the characteristics of the broad-leaved tree point clouds. An observation procedure down-samples the point clouds with the partial Fourier matrix. The regularized orthogonal matching pursuit algorithm (ROMP) finally reconstructs the original point clouds. The experimental results illustrate that the proposed scheme can preserve morphological attributes of the broad-leaved tree within a range of relative error: 0.0010%–3.3937%, and robustly extend to plot-level within a range of mean square error (MSE): 0.0063–0.2245.
Renjie Xu; Ting Yun; Lin Cao; Yunfei Liu. Compression and Recovery of 3D Broad-Leaved Tree Point Clouds Based on Compressed Sensing. Forests 2020, 11, 257 .
AMA StyleRenjie Xu, Ting Yun, Lin Cao, Yunfei Liu. Compression and Recovery of 3D Broad-Leaved Tree Point Clouds Based on Compressed Sensing. Forests. 2020; 11 (3):257.
Chicago/Turabian StyleRenjie Xu; Ting Yun; Lin Cao; Yunfei Liu. 2020. "Compression and Recovery of 3D Broad-Leaved Tree Point Clouds Based on Compressed Sensing." Forests 11, no. 3: 257.
Rubber trees in southern China are often impacted by natural disturbances that can result in a tilted tree body. Accurate crown segmentation for individual rubber trees from scanned point clouds is an essential prerequisite for accurate tree parameter retrieval. In this paper, three plots of different rubber tree clones, PR107, CATAS 7-20-59, and CATAS 8-7-9, were taken as the study subjects. Through data collection using ground-based mobile light detection and ranging (LiDAR), a voxelisation method based on the scanned tree trunk data was proposed, and deep images (i.e., images normally used for deep learning) were generated through frontal and lateral projection transform of point clouds in each voxel with a length of 8 m and a width of 3 m. These images provided the training and testing samples for the faster region-based convolutional neural network (Faster R-CNN) of deep learning. Consequently, the Faster R-CNN combined with the generated training samples comprising 802 deep images with pre-marked trunk locations was trained to automatically recognize the trunk locations in the testing samples, which comprised 359 deep images. Finally, the point clouds for the lower parts of each trunk were extracted through back-projection transform from the recognized trunk locations in the testing samples and used as the seed points for the region’s growing algorithm to accomplish individual rubber tree crown segmentation. Compared with the visual inspection results, the recognition rate of our method reached 100% for the deep images of the testing samples when the images contained one or two trunks or the trunk information was slightly occluded by leaves. For the complicated cases, i.e., multiple trunks or overlapping trunks in one deep image or a trunk appearing in two adjacent deep images, the recognition accuracy of our method was greater than 90%. Our work represents a new method that combines a deep learning framework with point cloud processing for individual rubber tree crown segmentation based on ground-based mobile LiDAR scanned data.
Jiamin Wang; Chen; Lin Cao; Feng An; Lianfeng Xue; Ting Yun; Wang; Cao; An; Xue; Yun; Xinxin Chen; Bangqian Chen. Individual Rubber Tree Segmentation Based on Ground-Based LiDAR Data and Faster R-CNN of Deep Learning. Forests 2019, 10, 793 .
AMA StyleJiamin Wang, Chen, Lin Cao, Feng An, Lianfeng Xue, Ting Yun, Wang, Cao, An, Xue, Yun, Xinxin Chen, Bangqian Chen. Individual Rubber Tree Segmentation Based on Ground-Based LiDAR Data and Faster R-CNN of Deep Learning. Forests. 2019; 10 (9):793.
Chicago/Turabian StyleJiamin Wang; Chen; Lin Cao; Feng An; Lianfeng Xue; Ting Yun; Wang; Cao; An; Xue; Yun; Xinxin Chen; Bangqian Chen. 2019. "Individual Rubber Tree Segmentation Based on Ground-Based LiDAR Data and Faster R-CNN of Deep Learning." Forests 10, no. 9: 793.
LiDAR (Light Detection and Ranging) technology has been increasingly implemented to assess the biophysical attributes of forest canopies. However, LiDAR-based estimation of tree biophysical attributes remains difficult mainly due to the occlusion of vegetative elements in multi-layered tree crowns. In this study, we developed a new algorithm along with a multiple-scan methodology to analyse the impact of occlusion on LiDAR-based estimates of tree leaf area. We reconstructed five virtual tree models using a computer graphic-based approach based on in situ measurements from multiple tree crowns, for which the position, size, orientation and area of all leaves were measured. Multi-platform LiDAR simulations were performed on these 3D tree models through a point-line intersection algorithm. An approach based on the Delaunay triangulation algorithm with automatic adaptive threshold selection was proposed to construct the scanned leaf surface from the simulated discrete LiDAR point clouds. In addition, the leaf area covered by laser beams in each layer was assessed in combination with the ratio and number of the scanned points. Quantitative comparisons of LiDAR scanning for the occlusion effects among various scanning approaches, including fixed-position scanning, multiple terrestrial LiDAR scanning and airborne-terrestrial LiDAR cross-scanning, were assessed on different target trees. The results showed that one simulated terrestrial LiDAR scan alongside the model tree captured only 25–38% of the leaf area of the tree crown. When scanned data were acquired from three simulated terrestrial LiDAR scans around one tree, the accuracy of the leaf area recovery rate reached 60–73% depending on the leaf area index, tree crown volume and leaf area density. When a supplementary airborne LiDAR scanning was included, occlusion was reduced and the leaf area recovery rate increased to 72–90%. Our study provides an approach for the measurement of total leaf area in tree crowns from simulated multi-platform LiDAR data and enables a quantitative assessment of occlusion metrics for various tree crown attributes under different scanning strategies.
Ting Yun; Lin Cao; Feng An; Bangqian Chen; Lianfeng Xue; Weizheng Li; Sylvain Pincebourde; Martin J. Smith; Markus P. Eichhorn. Simulation of multi-platform LiDAR for assessing total leaf area in tree crowns. Agricultural and Forest Meteorology 2019, 276-277, 107610 .
AMA StyleTing Yun, Lin Cao, Feng An, Bangqian Chen, Lianfeng Xue, Weizheng Li, Sylvain Pincebourde, Martin J. Smith, Markus P. Eichhorn. Simulation of multi-platform LiDAR for assessing total leaf area in tree crowns. Agricultural and Forest Meteorology. 2019; 276-277 ():107610.
Chicago/Turabian StyleTing Yun; Lin Cao; Feng An; Bangqian Chen; Lianfeng Xue; Weizheng Li; Sylvain Pincebourde; Martin J. Smith; Markus P. Eichhorn. 2019. "Simulation of multi-platform LiDAR for assessing total leaf area in tree crowns." Agricultural and Forest Meteorology 276-277, no. : 107610.
This paper presents a novel framework to extract metro tunnel cross sections (profiles) from Terrestrial Laser Scanning point clouds. The entire framework consists of two steps: tunnel central axis extraction and cross section determination. In tunnel central extraction, we propose a slice-based method to obtain an initial central axis, which is further divided into linear and nonlinear circular segments by an enhanced Random Sample Consensus (RANSAC) tunnel axis segmentation algorithm. This algorithm transforms the problem of hybrid linear and nonlinear segment extraction into a sole segmentation of linear elements defined at the tangent space rather than raw data space, significantly simplifying the tunnel axis segmentation. The extracted axis segments are then provided as input to the step of the cross section determination which generates the coarse cross-sectional points by intersecting a series of straight lines that rotate orthogonally around the tunnel axis with their local fitted quadric surface, i.e., cylindrical surface. These generated profile points are further refined and densified via solving a constrained nonlinear least squares problem. Our experiments on Nanjing metro tunnel show that the cross sectional fitting error is only 1.69 mm. Compared with the designed radius of the metro tunnel, the RMSE (Root Mean Square Error) of extracted cross sections’ radii only keeps 1.60 mm. We also test our algorithm on another metro tunnel in Shanghai, and the results show that the RMSE of radii only keeps 4.60 mm which is superior to a state-of-the-art method of 6.00 mm. Apart from the accurate geometry, our approach can maintain the correct topology among cross sections, thereby guaranteeing the production of geometric tunnel model without crack defects. Moreover, we prove that our algorithm is insensitive to the missing data and point density.
Zhen Cao; Dong Chen; Yufeng Shi; Zhenxin Zhang; Fengxiang Jin; Ting Yun; Sheng Xu; Zhizhong Kang; Liqiang Zhang. A Flexible Architecture for Extracting Metro Tunnel Cross Sections from Terrestrial Laser Scanning Point Clouds. Remote Sensing 2019, 11, 297 .
AMA StyleZhen Cao, Dong Chen, Yufeng Shi, Zhenxin Zhang, Fengxiang Jin, Ting Yun, Sheng Xu, Zhizhong Kang, Liqiang Zhang. A Flexible Architecture for Extracting Metro Tunnel Cross Sections from Terrestrial Laser Scanning Point Clouds. Remote Sensing. 2019; 11 (3):297.
Chicago/Turabian StyleZhen Cao; Dong Chen; Yufeng Shi; Zhenxin Zhang; Fengxiang Jin; Ting Yun; Sheng Xu; Zhizhong Kang; Liqiang Zhang. 2019. "A Flexible Architecture for Extracting Metro Tunnel Cross Sections from Terrestrial Laser Scanning Point Clouds." Remote Sensing 11, no. 3: 297.
Accurate and reliable information on tree volume distributions, which describe tree frequencies in volume classes, plays a key role in guiding timber harvest, managing carbon budgets, and supplying ecosystem services. Airborne Light Detection and Ranging (LiDAR) has the capability of offering reliable estimates of the distributions of structure attributes in forests. In this study, we predicted individual tree volume distributions over a subtropical forest of southeast China using airborne LiDAR data and field measurements. We first estimated the plot-level total volume by LiDAR-derived standard and canopy metrics. Then the performances of three Weibull parameter prediction methods, i.e., parameter prediction method (PPM), percentile-based parameter recover method (PPRM), and moment-based parameter recover method (MPRM) were assessed to estimate the Weibull scale and shape parameters. Stem density for each plot was calculated by dividing the estimated plot total volume using mean tree volume (i.e., mean value of distributions) derived from the LiDAR-estimated Weibull parameters. Finally, the individual tree volume distributions were generated by the predicted scale and shape parameters, and then scaled by the predicted stem density. The results demonstrated that, compared with the general models, the forest type-specific (i.e., coniferous forests, broadleaved forests, and mixed forests) models had relatively higher accuracies for estimating total volume and stem density, as well as predicting Weibull parameters, percentiles, and raw moments. The relationship between the predicted and reference volume distributions showed a relatively high agreement when the predicted frequencies were scaled to the LiDAR-predicted stem density (mean Reynolds error index eR = 31.47–54.07, mean Packalén error index eP = 0.14–0.21). In addition, the predicted individual tree volume distributions predicted by PPRM of (average mean eR = 37.75) performed the best, followed by MPRM (average mean eR = 40.43) and PPM (average mean eR = 41.22). This study demonstrated that the LiDAR can potentially offer improved estimates of the distributions of tree volume in subtropical forests.
Lin Cao; Zhengnan Zhang; Ting Yun; Guibin Wang; Honghua Ruan; Guanghui She. Estimating Tree Volume Distributions in Subtropical Forests Using Airborne LiDAR Data. Remote Sensing 2019, 11, 97 .
AMA StyleLin Cao, Zhengnan Zhang, Ting Yun, Guibin Wang, Honghua Ruan, Guanghui She. Estimating Tree Volume Distributions in Subtropical Forests Using Airborne LiDAR Data. Remote Sensing. 2019; 11 (1):97.
Chicago/Turabian StyleLin Cao; Zhengnan Zhang; Ting Yun; Guibin Wang; Honghua Ruan; Guanghui She. 2019. "Estimating Tree Volume Distributions in Subtropical Forests Using Airborne LiDAR Data." Remote Sensing 11, no. 1: 97.
Leaf attribute estimation is crucial for understanding photosynthesis, respiration, transpiration, and carbon and nutrient cycling in vegetation and evaluating the biological parameters of plants or forests. Terrestrial laser scanning (TLS) has the capability to provide detailed characterisations of individual trees at both the branch and leaf scales and to extract accurate structural parameters of stems and crowns. In this paper, we developed a computer graphic-based 3D point cloud segmentation approach for accurately and efficiently detecting tree leaves and their morphological features (i.e., leaf area and leaf angle distributions (leaf azimuthal angle and leaf inclination angle)) from single leaves. To this end, we adopted a sphere neighbourhood model with an adaptive radius to extract the central area points of individual leaves with different morphological structures and complex spatial distributions; meanwhile, four auxiliary criteria were defined to ensure the accuracy of the extracted central area points of individual leaf surfaces. Then, the density-based spatial clustering of applications with noise (DBSCAN) algorithm was used to cluster the central area points of leaves and to obtain the centre point corresponding to each leaf surface. We also achieved segmentation of individual leaf blades using an advanced 3D watershed algorithm based on the extracted centre point of each leaf surface and two morphology-related parameters. Finally, the leaf attributes (leaf area and leaf angle distributions) were calculated and assessed by analysing the segmented single-leaf point cloud. To validate the final results, the actual leaf area, leaf inclination and azimuthal angle data of designated leaves on the experimental trees were manually measured during field activities. In addition, a sensitivity analysis investigated the effect of the parameters in our segmentation algorithm. The results demonstrated that the segmentation accuracy of Ehretia macrophylla (94.0%) was higher than that of crape myrtle (90.6%) and Fatsia japonica (88.8%). The segmentation accuracy of Fatsia japonica was the lowest of the three experimental trees. In addition, the single-leaf area estimation accuracy for Ehretia macrophylla (95.39%) was still the highest among the three experimental trees, and the single-leaf area estimation accuracy for crape myrtle (91.92%) was lower than that for Ehretia macrophylla (95.39%) and Fatsia japonica (92.48%). Third, the method proposed in this paper provided accurate leaf inclination and azimuthal angles for the three experimental trees (Ehretia macrophylla: leaf inclination angle: R 2 = 0.908, RMSE = 6.806° and leaf azimuth angle: R 2 = 0.981, RMSE = 7.680°; crape myrtle: leaf inclination angle: R 2 = 0.901, RMSE = 8.365° and leaf azimuth angle: R 2 = 0.938, RMSE = 7.573°; Fatsia japonica: leaf inclination angle: R 2 = 0.849, RMSE = 6.158° and leaf azimuth angle: R 2 = 0.947, RMSE = 3.946°). The results indicate that the proposed method is effective and operational for providing accurate, detailed information on single leaves and vegetation structure from scanned data. This capability facilitates improvements in applications such as the estimation of leaf area, leaf angle distribution and biomass.
Qiangfa Xu; Lin Cao; Lianfeng Xue; Bangqian Chen; Feng An; Ting Yun. Extraction of Leaf Biophysical Attributes Based on a Computer Graphic-based Algorithm Using Terrestrial Laser Scanning Data. Remote Sensing 2018, 11, 15 .
AMA StyleQiangfa Xu, Lin Cao, Lianfeng Xue, Bangqian Chen, Feng An, Ting Yun. Extraction of Leaf Biophysical Attributes Based on a Computer Graphic-based Algorithm Using Terrestrial Laser Scanning Data. Remote Sensing. 2018; 11 (1):15.
Chicago/Turabian StyleQiangfa Xu; Lin Cao; Lianfeng Xue; Bangqian Chen; Feng An; Ting Yun. 2018. "Extraction of Leaf Biophysical Attributes Based on a Computer Graphic-based Algorithm Using Terrestrial Laser Scanning Data." Remote Sensing 11, no. 1: 15.
Leaf area is an important plant canopy structure parameter with important ecological significance. Light detection and ranging technology (LiDAR) with the application of a terrestrial laser scanner (TLS) is an appealing method for accurately estimating leaf area; however, the actual utility of this scanner depends largely on the efficacy of point cloud data (PCD) analysis. In this paper, we present a novel method for quantifying total leaf area within each tree canopy from PCD. Firstly, the shape, normal vector distribution and structure tensor of PCD features were combined with the semi-supervised support vector machine (SVM) method to separate various tree organs, i.e., branches and leaves. In addition, the moving least squares (MLS) method was adopted to remove ghost points caused by the shaking of leaves in the wind during the scanning process. Secondly, each target tree was scanned using two patterns, i.e., one scan and three scans around the canopy, to reduce the occlusion effect. Specific layer subdivision strategies according to the acquisition ranges of the scanners were designed to separate the canopy into several layers. Thirdly, 10% of the PCD was randomly chosen as an analytic dataset (ADS). For the ADS, an innovative triangulation algorithm with an assembly threshold was designed to transform these discrete scanning points into leaf surfaces and estimate the fractions of each foliage surface covered by the laser pulses. Then, a novel ratio of the point number to leaf area in each layer was defined and combined with the total number of scanned points to retrieve the total area of the leaves in the canopy. The quantified total leaf area of each tree was validated using laborious measurements with a LAI-2200 Plant Canopy Analyser and an LI-3000C Portable Area Meter. The results showed that the individual tree leaf area was accurately reproduced using our method from three registered scans, with a relative deviation of less than 10%. Nevertheless, estimations from only one scan resulted in a deviation of >25% in the retrieved individual tree leaf area due to the occlusion effect. Indeed, this study provides a novel connection between leaf area estimates and scanning sensor configuration and supplies an interesting method for estimating leaf area based on PCD.
Ting Yun; Feng An; Weizheng Li; Yuan Sun; Lin Cao; Lianfeng Xue. A Novel Approach for Retrieving Tree Leaf Area from Ground-Based LiDAR. Remote Sensing 2016, 8, 942 .
AMA StyleTing Yun, Feng An, Weizheng Li, Yuan Sun, Lin Cao, Lianfeng Xue. A Novel Approach for Retrieving Tree Leaf Area from Ground-Based LiDAR. Remote Sensing. 2016; 8 (11):942.
Chicago/Turabian StyleTing Yun; Feng An; Weizheng Li; Yuan Sun; Lin Cao; Lianfeng Xue. 2016. "A Novel Approach for Retrieving Tree Leaf Area from Ground-Based LiDAR." Remote Sensing 8, no. 11: 942.
In order to retrieve gap fraction, leaf inclination angle, and leaf area index (LAI) of subtropical forestry canopy, here we acquired forestry detailed information by means of hemispherical photography, terrestrial laser scanning, and LAI-2200 plant canopy analyzer. Meanwhile, we presented a series of image processing and computer graphics algorithms that include image and point cloud data (PCD) segmentation methods for branch and leaf classification and PCD features, such as normal vector, tangent plane extraction, and hemispherical projection method for PCD coordinate transformation. In addition, various forestry mathematical models were proposed to deduce forestry canopy indexes based on the radiation transfer model of Beer-Lambert law. Through the comparison of the experimental results on many plot samples, the terrestrial laser scanner- (TLS-) based index estimation method obtains results similar to digital hemispherical photograph (HP) and LAI-2200 plant canopy analyzer taken of the same stands and used for validation. It indicates that the TLS-based algorithm is able to capture the variability in LAI of forest stands with a range of densities, and there is a high chance to enhance TLS as a calibration tool for other devices.
Ting Yun; Weizheng Li; Yuan Sun; Lianfeng Xue. Study of Subtropical Forestry Index Retrieval Using Terrestrial Laser Scanning and Hemispherical Photography. Mathematical Problems in Engineering 2015, 2015, 1 -14.
AMA StyleTing Yun, Weizheng Li, Yuan Sun, Lianfeng Xue. Study of Subtropical Forestry Index Retrieval Using Terrestrial Laser Scanning and Hemispherical Photography. Mathematical Problems in Engineering. 2015; 2015 ():1-14.
Chicago/Turabian StyleTing Yun; Weizheng Li; Yuan Sun; Lianfeng Xue. 2015. "Study of Subtropical Forestry Index Retrieval Using Terrestrial Laser Scanning and Hemispherical Photography." Mathematical Problems in Engineering 2015, no. : 1-14.
The research is aimed at the development of an image processing system for classification of pathological area for medical images obtained from computed tomography (CT) scans. We proposed a novel semi-supervised image segmentation method based on the curvelet transform and SVM classfication. Firstly, through curvelet transform ultrasound images were decomposed into different directions and scales, the main distribution curvelet coefficients were extracted by cauchy model to reduce the algorithm time complexity, after inverse curvelet transform to obtaine a series of feature vectors from main distribution curvelet coefficients, then training samples and test samples were constructed; Secondly semi-supervised SVM classifier was designed, in order to reducing the weak classifier error rate, iteratively adjustment method was used to modify the SVM parameters, thus SVM strong classifier was constructed; Finally the expert manual tagging map were taken as reference standards, comparison with the existing method, experimental results shows that our algorithm is high anti-interference and has higher accuracy and effectiveness for ultrasound images pathological region segmentation.
Ting Yun; Yi Qing Xu; Lin Cao. Semi-Supervised Ultrasound Image Segmentation Based on Curvelet Features. Applied Mechanics and Materials 2012, 239-240, 104 -114.
AMA StyleTing Yun, Yi Qing Xu, Lin Cao. Semi-Supervised Ultrasound Image Segmentation Based on Curvelet Features. Applied Mechanics and Materials. 2012; 239-240 ():104-114.
Chicago/Turabian StyleTing Yun; Yi Qing Xu; Lin Cao. 2012. "Semi-Supervised Ultrasound Image Segmentation Based on Curvelet Features." Applied Mechanics and Materials 239-240, no. : 104-114.