Ground Profile Recovery from Aerial 3D LiDAR-Based Maps

Sabirova, Adelya; Rassabin, Maksim; Fedorenko, Roman; Afanasyev, Ilya

doi:10.23919/fruct.2019.8711928

Cited by 6 publications

(2 citation statements)

References 40 publications

(52 reference statements)

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“…This superior performance is attributed to the PTD algorithm's efficacy in distinguishing between ground and non-ground points, effectively capturing the terrain and plant surface features more accurately. The precision in separating the ground points is crucial for high-density crop height extraction, as demonstrated by the PTD algorithm's higher accuracy; this further verifies the effectiveness of the PTD algorithm [34,40].…”

Section: Cotton Plant Height Information Extractionmentioning

confidence: 65%

Comparative Evaluation of the Performance of the PTD and CSF Algorithms on UAV LiDAR Data for Dynamic Canopy Height Modeling in Densely Planted Cotton

Yang,

Wu,

et al. 2024

Agronomy

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This study introduces a novel methodology for the dynamic extraction of information on cotton growth in terms of height utilizing the DJI Zenmuse L1 LiDAR sensor mounted onto a DJI Matrice 300 RTK Unmanned Aerial Vehicle (UAV), aimed at enhancing the precision and efficiency of growth monitoring within the realm of precision agriculture. Employing the Progressive TIN Densification (PTD) and Cloth Simulation Filter (CSF) algorithms, combined with Kriging interpolation, we generated Canopy Height Models (CHMs) to extract the cotton heights at two key agricultural sites: Zengcheng and Tumxuk. Our analysis reveals that the PTD algorithm significantly outperforms the CSF method in terms of accuracy, with its R2 values indicating a superior model fit for height extraction across different growth stages (Zengcheng: 0.71, Tumxuk: 0.82). Through meticulous data processing and cluster analysis, this study not only identifies the most effective algorithm for accurate height extraction but also provides detailed insights into the dynamic growth patterns of cotton varieties across different geographical regions. The findings highlight the critical role of UAV remote sensing in enabling large-scale, high-precision monitoring of crop growth, which is essential for the optimization of agricultural practices such as precision fertilization and irrigation. Furthermore, the study demonstrates the potential of UAV technology to select superior cotton varieties by analyzing their growth dynamics, offering valuable guidance for cotton breeding and cultivation.

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Section: Cotton Plant Height Information Extractionmentioning

confidence: 65%

Comparative Evaluation of the Performance of the PTD and CSF Algorithms on UAV LiDAR Data for Dynamic Canopy Height Modeling in Densely Planted Cotton

Yang,

Wu,

et al. 2024

Agronomy

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show abstract

“…In order to cope with complex and changing real-world situations, the CSF algorithm contains seven parameters that have to be adjusted according to the application at hand: rigidness, time step, grid resolution, distance threshold, height difference, maximum iteration number, and one optional parameter, the steep slope fit factor. An example of how to fix these parameters is given in [39].…”

Section: Filtering Algorithmmentioning

confidence: 99%

Classification of Terrestrial Laser Scanner Point Clouds: A Comparison of Methods for Landslide Monitoring from Mathematical Surface Approximation

Kermarrec

Yang²,

Czerwonka‐Schröder

2022

Remote Sensing

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Terrestrial laser scanners (TLS) are contact-free measuring sensors that record dense point clouds of objects or scenes by acquiring coordinates and an intensity value for each point. The point clouds are scattered and noisy. Performing a mathematical surface approximation instead of working directly on the point cloud is an efficient way to reduce the data storage and structure the point clouds by transforming “data” to “information”. Applications include rigorous statistical testing for deformation analysis within the context of landslide monitoring. In order to reach an optimal approximation, classification and segmentation algorithms can identify and remove inhomogeneous structures, such as trees or bushes, to obtain a smooth and accurate mathematical surface of the ground. In this contribution, we compare methods to perform the classification of TLS point clouds with the aim of guiding the reader through the existing algorithms. Besides the traditional point cloud filtering methods, we will analyze machine learning classification algorithms based on the manual extraction of point cloud features, and a deep learning approach with automatic extraction of features called PointNet++. We have intentionally chosen strategies easy to implement and understand so that our results are reproducible for similar point clouds. We show that each method has advantages and drawbacks, depending on user criteria, such as the computational time, the classification accuracy needed, whether manual extraction is performed or not, and if prior information is required. We highlight that filtering methods are advantageous for the application at hand and perform a mathematical surface approximation as an illustration. Accordingly, we have chosen locally refined B-splines, which were shown to provide an optimal and computationally manageable approximation of TLS point clouds.

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Implementation of Autonomous Visual Detection, Tracking and Landing for AR.Drone 2.0 Quadcopter

Respall

Sellami

Afanasyev

2019

2019 12th International Conference on Developments in eSystems Engineering (DeSE)

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Ground Profile Recovery from Aerial 3D LiDAR-Based Maps

Cited by 6 publications

References 40 publications

Comparative Evaluation of the Performance of the PTD and CSF Algorithms on UAV LiDAR Data for Dynamic Canopy Height Modeling in Densely Planted Cotton

Comparative Evaluation of the Performance of the PTD and CSF Algorithms on UAV LiDAR Data for Dynamic Canopy Height Modeling in Densely Planted Cotton

Classification of Terrestrial Laser Scanner Point Clouds: A Comparison of Methods for Landslide Monitoring from Mathematical Surface Approximation

Implementation of Autonomous Visual Detection, Tracking and Landing for AR.Drone 2.0 Quadcopter

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