Evaluating the Accuracy of Dem Generation Algorithms From Uav Imagery

Ruiz, J.J.; Diaz-Mas, L.; Pérez, Fernando; Viguria, Antidio

doi:10.5194/isprsarchives-xl-1-w2-333-2013

Cited by 32 publications

(25 citation statements)

References 8 publications

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“…As expected, indirectly geo-referenced imagery showed smaller residuals than the directly geo-referenced one. Horizontal RMSEs for indirectly geo-referenced imagery ranged from 0.058 to 0.089 m, whereas vertical RMSEs ranged from 0.046 to 0.075 m. In contrast to that, horizontal RMSEs for directly geo-referenced imagery ranged from 0.375 to 0.430 m, whereas vertical RMSEs ranged from 0.273 to 0.379 m. The accuracies of both methods are in accordance with the findings of Turner et al [55] and Ruiz et al [56], although vertical accuracy performs slightly better than expected. GCP-based accuracy assessment for directly geo-referenced imagery at Z39 and Z58 was not performed.…”

Section: Image Processingsupporting

confidence: 75%

Combined Spectral and Spatial Modeling of Corn Yield Based on Aerial Images and Crop Surface Models Acquired with an Unmanned Aircraft System

2014

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Precision Farming (PF) management strategies are commonly based on estimations of within-field yield potential, often derived from remotely-sensed products, e.g., Vegetation Index (VI) maps. These well-established means, however, lack important information, like crop height. Combinations of VI-maps and detailed 3D Crop Surface Models (CSMs) enable advanced methods for crop yield prediction. This work utilizes an Unmanned Aircraft System (UAS) to capture standard RGB imagery datasets for corn grain yield prediction at three early-to mid-season growth stages. The imagery is processed into simple VI-orthoimages for crop/non-crop classification and 3D CSMs for crop height determination at different spatial resolutions. Three linear regression models are tested on their prediction ability using site-specific (i) unclassified mean heights, (ii) crop-classified mean heights and (iii) a combination of crop-classified mean heights with according crop coverages. The models show determination coefficients R 2 of up to 0.74, whereas model (iii) performs best with imagery captured at the end of stem elongation and intermediate spatial resolution (0.04 m·px −1 ). Following these results, combined spectral and spatial modeling, based on aerial images and CSMs, proves to be a suitable method for mid-season corn yield prediction.

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Section: Image Processingsupporting

confidence: 75%

Combined Spectral and Spatial Modeling of Corn Yield Based on Aerial Images and Crop Surface Models Acquired with an Unmanned Aircraft System

2014

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“…The authors highlight issues with GPS accuracy as a main source of crop height error, as well as suggesting using higher quality cameras for image collection. Ruiz et al [33] also found the SfM algorithms suffer from errors present in GPS datasets. Ground-based Control Points (GCP) located within the scene are recommended as best-practice for spatial accuracy and minimisation of model error.…”

Section: Structure-from-motion and Crop Modellingmentioning

confidence: 99%

High Throughput Field Phenotyping of Wheat Plant Height and Growth Rate in Field Plot Trials Using UAV Based Remote Sensing

et al. 2016

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Abstract:There is a growing need to increase global crop yields, whilst minimising use of resources such as land, fertilisers and water. Agricultural researchers use ground-based observations to identify, select and develop crops with favourable genotypes and phenotypes; however, the ability to collect rapid, high quality and high volume phenotypic data in open fields is restricting this. This study develops and assesses a method for deriving crop height and growth rate rapidly from multi-temporal, very high spatial resolution (1 cm/pixel), 3D digital surface models of crop field trials produced via Structure from Motion (SfM) photogrammetry using aerial imagery collected through repeated campaigns flying an Unmanned Aerial Vehicle (UAV) with a mounted Red Green Blue (RGB) camera. We compare UAV SfM modelled crop heights to those derived from terrestrial laser scanner (TLS) and to the standard field measurement of crop height conducted using a 2 m rule. The most accurate UAV-derived surface model and the TLS both achieve a Root Mean Squared Error (RMSE) of 0.03 m compared to the existing manual 2 m rule method. The optimised UAV method was then applied to the growing season of a winter wheat field phenotyping experiment containing 25 different varieties grown in 27 m 2 plots and subject to four different nitrogen fertiliser treatments. Accuracy assessments at different stages of crop growth produced consistently low RMSE values (0.07, 0.02 and 0.03 m for May, June and July, respectively), enabling crop growth rate to be derived from differencing of the multi-temporal surface models. We find growth rates range from −13 mm/day to 17 mm/day. Our results clearly display the impact of variable nitrogen fertiliser rates on crop growth. Digital surface models produced provide a novel spatial mapping of crop height variation both at the field scale and also within individual plots. This study proves UAV based SfM has the potential to become a new standard for high-throughput phenotyping of in-field crop heights.

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“…The quality of this model depends on many factors and parameters, such as the initial images quality, the availability of principal points and coordinates of vertical and horizontal control survey network points, flight altitude, overlaps and camera resolution. The final DSM accuracy is influenced by the variations of these factors and parameters (Ruiz et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Uav Aerial Survey: Accuracy Estimation for Automatically Generated Dense Digital Surface Model and Orthothoto Plan

Altyntsev

Arbuzov

Попов

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:A dense digital surface model is one of the products generated by using UAV aerial survey data. Today more and more specialized software are supplied with modules for generating such kind of models. The procedure for dense digital model generation can b e completely or partly automated. Due to the lack of reliable criterion of accuracy estimation it is rather complicated to judge the generation validity of such models. One of such criterion can be mobile laser scanning data as a source for the detailed accu racy estimation of the dense digital surface model generation. These data may be also used to estimate the accuracy of digital orthophoto plans created by using UAV aerial survey data. The results of accuracy estimation for both kinds of products are presented in the paper.

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Evaluating the Accuracy of Dem Generation Algorithms From Uav Imagery

Cited by 32 publications

References 8 publications

Combined Spectral and Spatial Modeling of Corn Yield Based on Aerial Images and Crop Surface Models Acquired with an Unmanned Aircraft System

Combined Spectral and Spatial Modeling of Corn Yield Based on Aerial Images and Crop Surface Models Acquired with an Unmanned Aircraft System

High Throughput Field Phenotyping of Wheat Plant Height and Growth Rate in Field Plot Trials Using UAV Based Remote Sensing

Uav Aerial Survey: Accuracy Estimation for Automatically Generated Dense Digital Surface Model and Orthothoto Plan

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