Second Iteration of Photogrammetric Processing to Refine Image Orientation with Improved Tie-Points †

Giang, Nguyen Truong; Muller, Jean-Michaël; Rupnik, Ewelina; Thom, Christian; Pierrot-Deseilligny, Marc

doi:10.3390/s18072150

Cited by 10 publications

(8 citation statements)

References 36 publications

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“…An orthomosaic is a detailed and geometrically accurate image of an area, composed of multiple photos that have been orthorectified. Within this framework, once the In this SfM workflow, the low accuracy and spatial distribution of tie points sometimes can hinder the estimation of camera orientation, potentially leading to nonlinear deformations within the georeferenced sparse cloud [54]. To tackle this challenge, this study employed a novel optimization method that is designed to enhance the georeferencing process of the sparse cloud [50].…”

Section: Sfm-mvs Methods Outlinementioning

confidence: 99%

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Optimizing Drone-Based Surface Models for Prescribed Fire Monitoring

Mestre-Runge,

Ludwig,

Sebastià

et al. 2023

Fire

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Prescribed burning and pyric herbivory play pivotal roles in mitigating wildfire risks, underscoring the imperative of consistent biomass monitoring for assessing fuel load reductions. Drone-derived surface models promise uninterrupted biomass surveillance but require complex photogrammetric processing. In a Mediterranean mountain shrubland burning experiment, we refined a Structure from Motion (SfM) and Multi-View Stereopsis (MVS) workflow to diminish biases in 3D modeling and RGB drone imagery-based surface reconstructions. Given the multitude of SfM-MVS processing alternatives, stringent quality oversight becomes paramount. We executed the following steps: (i) calculated Root Mean Square Error (RMSE) between Global Navigation Satellite System (GNSS) checkpoints to assess SfM sparse cloud optimization during georeferencing; (ii) evaluated elevation accuracy by comparing the Mean Absolute Error (MAE) of six surface and thirty terrain clouds against GNSS readings and known box dimensions; and (iii) complemented a dense cloud quality assessment with density metrics. Balancing overall accuracy and density, we selected surface and terrain cloud versions for high-resolution (2 cm pixel size) and accurate (DSM, MAE = 57 mm; DTM, MAE = 48 mm) Digital Elevation Model (DEM) generation. These DEMs, along with exceptional height and volume models (height, MAE = 12 mm; volume, MAE = 909.20 cm3) segmented by reference box true surface area, substantially contribute to burn impact assessment and vegetation monitoring in fire management systems.

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Section: Sfm-mvs Methods Outlinementioning

confidence: 99%

“…Throughout each step of this standard SfM-MVS process, there are multiple configurable parameters and options that influence the outcome in terms of sparse cloud accuracy [54], dense point cloud quality [38], DEM grid structure [42], and orthomosaic quality [50].…”

Section: Sfm-mvs Methods Outlinementioning

confidence: 99%

Optimizing Drone-Based Surface Models for Prescribed Fire Monitoring

Mestre-Runge,

Ludwig,

Sebastià

et al. 2023

Fire

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“…Least squares matching does not significantly improve the matching accuracy of automatically generated tie points 1,15 . In addition, photogrammetric measurements with automatically or manually selected tie points have a close level of accuracy 16 .…”

Section: Introductionmentioning

confidence: 95%

Feature Point-Based Dense Image Matching Algorithm for 3-D Capture in Terrestrial Applications

Altuntas¹

2022

J. of Applied Sciences

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Background and Objective: Image matching is a bottleneck that must be surpassed in photogrammetric measurement, camera calibration and computer vision. This study investigated the performances of the well-known feature point detectors in automatic matching. Materials and Methods: A large base-to-height ratio of stereo images creates perspective distortion and the selection of an objectʼs small shape properties from the image becomes difficult. Therefore, a large base-to-height ratio affects matching and measurement accuracy in photogrammetry. The relative variations on the scale of stereo images also make it difficult to create conjugate points between them. Different base-to-height and various scale stereo images were evaluated to compare the matching performance of these operators. Results: The results show that the number of matched feature points decreases when the base-to-height ratio of the images is increased. The SIFT, ASIFT and SURF operators did not match the images with a base-to-height ratio larger than 1.5 and a scale change of more than three times. Finally, ASIFT generated more matched points than SIFT and SURF. Conclusion: These findings are useful for automatic three-dimensional measurement from stereo images. Three-dimensional measurement can be performed with fewer images without any force on computer capacity. The imaging positions and point of view angles for multiview evaluations should also be planned according to the limitations of these operators.

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“…The post processing of the FORMap result for extracting a new set of tie-points that are optimized for photogrammetric processing could be considered for high-precision applications (Truong Giang et al, 2018, Nguyen et al, 2017.…”

Section: The Improvement Of Tie Points Extractionmentioning

confidence: 99%

Formap: A Simple and Fast Photogrammetry Framework for Direct Geo-Referencing Systems

Nguyen¹,

Seube²

2019

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

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Abstract. This paper presents FORMap (Fast Ortho Mapping) a simple, automatic, fast and accurate commercial photogrammetry processing software for Unmanned Aerial Vehicles (UAV) imagery equiped with Direct Georeferencing (DG) technology. DG technique allows user to directly geo-reference the acquisition without the use of Ground Control Points (GCP) by providing image external orientation (EO) parameters in a mapping frame. However, it requires a sensor of relatively high quality to provide an accurate EO with each image shot, which is somehow limited by the light weight of UAV payloads. FORMap makes use of EO information delivered by DG as an a priori information to accelerate its photogrammetric processing. We present the functionalities and some application of FORMap in the field of UAV mapping. We evaluate its accuracy and its robustness on several datasets. Test result shows that FORMap is robust for 3D scene reconstruction despite of inaccuracies of DG input data. It is also faster than standard digital photogrammetry solution based on SfM (Structure from Motion) approach and can provide orthophotos and dense point cloud in quasi real-time.

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Second Iteration of Photogrammetric Processing to Refine Image Orientation with Improved Tie-Points †

Cited by 10 publications

References 36 publications

Optimizing Drone-Based Surface Models for Prescribed Fire Monitoring

Optimizing Drone-Based Surface Models for Prescribed Fire Monitoring

Feature Point-Based Dense Image Matching Algorithm for 3-D Capture in Terrestrial Applications

Formap: A Simple and Fast Photogrammetry Framework for Direct Geo-Referencing Systems

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