The Influence of Ground Control Points Configuration and Camera Calibration for DTM and Orthomosaic Generation Using Imagery Obtained From a Low-Cost Uav

Garcia, Marcos Vinícius Yodono; Oliveira, Henrique Cândido de

doi:10.5194/isprs-annals-v-1-2020-239-2020

Cited by 7 publications

(4 citation statements)

References 19 publications

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“…Here point clouds were built from SfM-processed images [45]. As the sensor used was geometrically calibrated via photogrammetry, the control points defined yielded a high-precision three-dimensional model of the geo-referenced crop [46]. The relationship between pruning weight and the respective point clouds was calculated in the laboratory using linear regression.…”

Section: Discussionmentioning

confidence: 99%

Vineyard Pruning Weight Prediction Using 3D Point Clouds Generated from UAV Imagery and Structure from Motion Photogrammetry

et al. 2021

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In viticulture, information about vine vigour is a key input for decision-making in connection with production targets. Pruning weight (PW), a quantitative variable used as indicator of vegetative vigour, is associated with the quantity and quality of the grapes. Interest has been growing in recent years around the use of unmanned aerial vehicles (UAVs) or drones fitted with remote sensing facilities for more efficient crop management and the production of higher quality wine. Current research has shown that grape production, leaf area index, biomass, and other viticulture variables can be estimated by UAV imagery analysis. Although SfM lowers costs, saves time, and reduces the amount and type of resources needed, a review of the literature revealed no studies on its use to determine vineyard pruning weight. The main objective of this study was to predict PW in vineyards from a 3D point cloud generated with RGB images captured by a standard drone and processed by SfM. In this work, vertical and oblique aerial images were taken in two vineyards of Godello and Mencía varieties during the 2019 and 2020 seasons using a conventional Phantom 4 Pro drone. Pruning weight was measured on sampling grids comprising 28 calibration cells for Godello and 59 total cells for Mencía (39 calibration cells and 20 independent validation). The volume of vegetation (V) was estimated from the generated 3D point cloud and PW was estimated by linear regression analysis taking V as predictor variable. When the results were leave-one-out cross-validated (LOOCV), the R2 was found to be 0.71 and the RMSE 224.5 (g) for the PW estimate in Mencía 2020, calculated for the 39 calibration cells on the grounds of oblique images. The regression analysis results for the 20 validation samples taken independently of the rest (R2 = 0.62; RMSE = 249.3 g) confirmed the viability of using the SfM as a fast, non-destructive, low-cost procedure for estimating pruning weight.

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Section: Discussionmentioning

confidence: 99%

Vineyard Pruning Weight Prediction Using 3D Point Clouds Generated from UAV Imagery and Structure from Motion Photogrammetry

et al. 2021

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“…Georeferencing, either direct or indirect, provides information about Exterior Orientation Parameters (EOPs). The EOPs information stored in External Information File (EXIF), which is a type of geotagged metadata along each individual image, is based upon onboard GNSS [ 37 ]. In this research, UAV images are captured by using DJI Phantom 4 Advanced.…”

Section: Methodsmentioning

confidence: 99%

Assessment of DSM Based on Radiometric Transformation of UAV Data

Chaudhry

Ahmad

Gulzar

et al. 2021

Sensors

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Unmanned Aerial Vehicle (UAV) is one of the latest technologies for high spatial resolution 3D modeling of the Earth. The objectives of this study are to assess low-cost UAV data using image radiometric transformation techniques and investigate its effects on global and local accuracy of the Digital Surface Model (DSM). This research uses UAV Light Detection and Ranging (LIDAR) data from 80 meters and UAV Drone data from 300 and 500 meters flying height. RAW UAV images acquired from 500 meters flying height are radiometrically transformed in Matrix Laboratory (MATLAB). UAV images from 300 meters flying height are processed for the generation of 3D point cloud and DSM in Pix4D Mapper. UAV LIDAR data are used for the acquisition of Ground Control Points (GCP) and accuracy assessment of UAV Image data products. Accuracy of enhanced DSM with DSM generated from 300 meters flight height were analyzed for point cloud number, density and distribution. Root Mean Square Error (RMSE) value of Z is enhanced from ±2.15 meters to 0.11 meters. For local accuracy assessment of DSM, four different types of land covers are statistically compared with UAV LIDAR resulting in compatibility of enhancement technique with UAV LIDAR accuracy.

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“…In this study, the GCPs were used as the targets for georeferencing. They are necessary when conducting aerial surveys since they bring the projected to the spatial coordinate system and improve the accuracy of the data obtained (Garcia and Oliveira, 2020;Geavis, 2020). Moreover, they make it possible for real-world measurements to be obtained from the georeferenced spatial data.…”

Section: Georeferencingmentioning

confidence: 99%

Classification of 3D UAS-SfM Point Clouds in the Urban Environment

Ntuli,

Forbes

2023

SA J of Geomatics

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The classification of three-dimensional (3D) point clouds derived through the use of cost-effective and time-efficient photogrammetric technologies can provide helpful information for applications, particularly in the mapping context. This paper presents a practical study of 3D Unmanned Aerial System (UAS) – Structure-from-Motion (SfM) point cloud classification using mainly open-source software. Following a supervised classification approach that makes use of only the dimensionality of points, the entire scene was classified into three land-cover categories: ground, high vegetation, and buildings. By applying the above-mentioned approach, the level of competence in classifying a 3D point cloud of a heterogeneous scene situated in the University of KwaZulu-Natal, South Africa, was evaluated. The resulting overall classification accuracy of 81.3%, with a Kappa coefficient of 0.70, was determined by means of a confusion matrix. The results achieved indicate the potential use of open-source software and 3D UAS-SfM point cloud classification in mapping and monitoring complex environments and in other applications that might arise.

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The Influence of Ground Control Points Configuration and Camera Calibration for DTM and Orthomosaic Generation Using Imagery Obtained From a Low-Cost Uav

Cited by 7 publications

References 19 publications

Vineyard Pruning Weight Prediction Using 3D Point Clouds Generated from UAV Imagery and Structure from Motion Photogrammetry

Vineyard Pruning Weight Prediction Using 3D Point Clouds Generated from UAV Imagery and Structure from Motion Photogrammetry

Assessment of DSM Based on Radiometric Transformation of UAV Data

Classification of 3D UAS-SfM Point Clouds in the Urban Environment

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