Multisource forest point cloud registration with semantic-guided keypoints and robust RANSAC mechanisms

Dai, Wei; Kan, Hongyang; Tan, Renchun; Wang, Jinhu; Guan, Qingfeng; Zhu, Ningning; Wen, Xiao-Yong; Dong, Zhen

doi:10.1016/j.jag.2022.103105

Cited by 5 publications

(2 citation statements)

References 42 publications

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“…ULS is able to capture information on the top of the tree, the ground height, and the highest point of the tree, which are important for correctly calculating H. The combination of TLS and ULS data can be used to obtain complete information on the sample plots. First, the collected multi-station TLS point clouds were co-registered to the same coordinate systems, then the TLS and ULS data were co-registered using the Random Sample Consensus (RANSAC) method [13]. The average registration residual for the TLS-to-TLS scenario was 0.049 m, while for the ULS-to-ALS scenario it was 0.299 m. The fused point cloud was then used to automatically perform an accurate individual tree extraction [14].…”

Section: Construction Of Field Plotsmentioning

confidence: 99%

Investigating Dual-Source Satellite Image Data and ALS Data for Estimating Aboveground Biomass

Fan,

Tian,

Knoke

et al. 2024

Remote Sensing

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Accurate estimation of above-ground biomass (AGB) in forested areas is essential for studying forest ecological functions, surface carbon cycling, and global carbon balance. Over the past decade, models that harness the distinct features of multi-source remote sensing observations for estimating AGB have gained significant popularity. It is worth exploring the differences in model performance by using simple and fused data. Additionally, quantitative estimation of the impact of high-cost laser point clouds on satellite imagery of varying costs remains largely unexplored. To address these challenges, model performance and cost must be considered comprehensively. We propose a comprehensive assessment based on three perspectives (i.e., performance, potential and limitations) for four typical AGB-estimation models. First, different variables are extracted from the multi-source and multi-resolution data. Subsequently, the performance of four regression methods is tested for AGB estimation with diverse indicator combinations. Experimental results prove that the combination of multi-source data provides a highly accurate AGB regression model. The proposed regression and variables rating approaches can flexibly integrate other data sources for modeling. Furthermore, the data cost is discussed against the AGB model performance. Our study demonstrates the potential of using low-cost satellite data to provide a rough AGB estimation for larger areas, which can allow different remote sensing data to meet different needs of forest management decisions.

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Section: Construction Of Field Plotsmentioning

confidence: 99%

Investigating Dual-Source Satellite Image Data and ALS Data for Estimating Aboveground Biomass

Fan,

Tian,

Knoke

et al. 2024

Remote Sensing

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show abstract

“…Also, it is robust to noise and outliers [39]. Dai et al [40] utilized semantic key points and the RANSAC algorithm for TLS-TLS and TLS-UAV point cloud registration. The results indicated that the proposed framework could improve the practicality and efficiency of multi-source data matching.…”

Section: Introductionmentioning

confidence: 99%

An Improved RANSAC-ICP Method for Registration of SLAM and UAV-LiDAR Point Cloud at Plot Scale

Zhang,

Wang,

Wang

et al. 2024

Forests

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Simultaneous Localization and Mapping (SLAM) using LiDAR technology can acquire the point cloud below the tree canopy efficiently in real time, and the Unmanned Aerial Vehicle LiDAR (UAV-LiDAR) can derive the point cloud of the tree canopy. By registering them, the complete 3D structural information of the trees can be obtained for the forest inventory. To this end, an improved RANSAC-ICP algorithm for registration of SLAM and UAV-LiDAR point cloud at plot scale is proposed in this study. Firstly, the point cloud features are extracted and transformed into 33-dimensional feature vectors by using the feature descriptor FPFH, and the corresponding point pairs are determined by bidirectional feature matching. Then, the RANSAC algorithm is employed to compute the transformation matrix based on the reduced set of feature points for coarse registration of the point cloud. Finally, the iterative closest point algorithm is used to iterate the transformation matrix to achieve precise registration of the SLAM and UAV-LiDAR point cloud. The proposed algorithm is validated on both coniferous and broadleaf forest datasets, with an average mean absolute distance (MAD) of 11.332 cm for the broadleaf forest dataset and 6.150 cm for the coniferous forest dataset. The experimental results show that the proposed method in this study can be effectively applied to the forest plot scale for the precise alignment of multi-platform point clouds.

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A Local Shape Descriptor Designed for Registration of Terrestrial Point Clouds

Tao,

Lu,

Chen

et al. 2024

IEEE Trans. Geosci. Remote Sensing

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Multisource forest point cloud registration with semantic-guided keypoints and robust RANSAC mechanisms

Cited by 5 publications

References 42 publications

Investigating Dual-Source Satellite Image Data and ALS Data for Estimating Aboveground Biomass

Investigating Dual-Source Satellite Image Data and ALS Data for Estimating Aboveground Biomass

An Improved RANSAC-ICP Method for Registration of SLAM and UAV-LiDAR Point Cloud at Plot Scale

A Local Shape Descriptor Designed for Registration of Terrestrial Point Clouds

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