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Optimising drone flight planning for measuring horticultural tree crop structure / Yu-Hsuan Tu in ISPRS Journal of photogrammetry and remote sensing, vol 160 (February 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 160 (February 2020) . - pp 83 - 96 Titre : Optimising drone flight planning for measuring horticultural tree crop structure Type de document : Article/Communication Auteurs : Yu-Hsuan Tu, Auteur ; Stuart Phinn, Auteur ; Kasper Johansen, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 83 - 96 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes descripteurs IGN] correction d'image [Termes descripteurs IGN] détection d'arbres [Termes descripteurs IGN] distorsion d'image [Termes descripteurs IGN] étalonnage de chambre métrique [Termes descripteurs IGN] horticulture [Termes descripteurs IGN] image de drone [Termes descripteurs IGN] MicMac [Termes descripteurs IGN] obturateur [Termes descripteurs IGN] photogrammétrie aérienne [Termes descripteurs IGN] plan de vol [Termes descripteurs IGN] point d'appui [Termes descripteurs IGN] qualité d'image [Termes descripteurs IGN] Queensland (Australie) Résumé : (Auteur) In recent times, multi-spectral drone imagery has proved to be a useful tool for measuring tree crop canopy structure. In this context, establishing the most appropriate flight planning variable settings is an essential consideration due to their controls on the quality of the imagery and derived maps of tree and crop biophysical properties. During flight planning, variables including flight altitude, image overlap, flying direction, flying speed and solar elevation, require careful consideration in order to produce the most suitable drone imagery. Previous studies have assessed the influence of individual variables on image quality, but the interaction of multiple variables has yet to be examined. This study assesses the influence of several flight variables on measures of data quality in each processing step, i.e. photo alignment, point cloud densification, 3D model building, and ortho-mosaicking. The analysis produced a drone flight planning and image processing workflow that delivers accurate measurements of tree crops, including the tie point quality, densified point cloud density, and the measurement accuracy of height and plant projective cover derived from individual trees within a commercial avocado orchard. Results showed that flying along the hedgerow, at high solar elevation and with low image pitch angles improved the data quality. Optimal flying speed needs to be set to achieve the required forward overlap. The impacts of each image acquisition variable are discussed in detail and protocols for flight planning optimisation for three scenarios with different drone settings are suggested. Establishing protocols that deliver optimal image acquisitions for the collection of drone data over horticultural tree crops, will create greater confidence in the accuracy of subsequent algorithms and resultant maps of biophysical properties. Numéro de notice : A2020-044 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.12.006 date de publication en ligne : 18/12/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.12.006 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94524 [article]

Modelling the orthoimage accuracy using DEM accuracy and off-nadir angle / Altan Yilmaz in Geocarto international, Vol 35 n° 1 ([02/01/2020])  

Public [article]  inGeocarto international > Vol 35 n° 1 [02/01/2020] . - pp 1 - 16 Titre : Modelling the orthoimage accuracy using DEM accuracy and off-nadir angle Type de document : Article/Communication Auteurs : Altan Yilmaz, Auteur ; Mustafa Erdogan, Auteur Année de publication : 2020 Article en page(s) : pp 1 - 16 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes descripteurs IGN] angle nadiral [Termes descripteurs IGN] centrale inertielle [Termes descripteurs IGN] erreur [Termes descripteurs IGN] erreur moyenne quadratique [Termes descripteurs IGN] modèle empirique [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] orthoimage [Termes descripteurs IGN] planimétrie [Termes descripteurs IGN] point d'appui Résumé : (auteur) Orthoimages are differentially rectified images that are corrected for the distortions caused especially by image tilt and topographic relief. The orientation, digital elevation model (DEM) and off-nadir angle plays an important role in orthoimage accuracy. The orientation error mostly occurs due to the quality and distribution of the ground control points. In this study, an attempt has been made to model the remaining errors by keeping the orientation error constant. To model the accuracy, orthoimages are produced with eight DEMs having different accuracies and are assessed using 50 check points. As the theoretical model cannot reflect the real world exactly, an empirical model is used for estimating the orthoimage accuracy. This proposed model was validated by another dataset. It is concluded that statistically there is no significant difference between the calculated model and real planimetric errors. The proposed model can be used in predicting orthoimage accuracy provided that the DEM accuracy and off-nadir angles of the points are known. Numéro de notice : A2020-016 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1493157 date de publication en ligne : 12/09/2018 En ligne : https://doi.org/10.1080/10106049.2018.1493157 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94409 [article]

Combining thermal imaging with photogrammetry of an active volcano using UAV: an example from Stromboli, Italy / Zoë E. Wakeford in Photogrammetric record, vol 34 n° 168 (December 2019)  

Public [article]  inPhotogrammetric record > vol 34 n° 168 (December 2019) . - pp 445 - 466 Titre : Combining thermal imaging with photogrammetry of an active volcano using UAV: an example from Stromboli, Italy Type de document : Article/Communication Auteurs : Zoë E. Wakeford, Auteur ; Magda Chmielewska, Auteur ; Malcolm J. Hole, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 445 - 466 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] données GNSS [Termes descripteurs IGN] image de drone [Termes descripteurs IGN] image RVB [Termes descripteurs IGN] image thermique [Termes descripteurs IGN] modélisation 3D [Termes descripteurs IGN] photogrammétrie numérique [Termes descripteurs IGN] point d'appui [Termes descripteurs IGN] risque naturel [Termes descripteurs IGN] spectre électromagnétique [Termes descripteurs IGN] Stromboli (volcan) [Termes descripteurs IGN] surveillance géologique Résumé : (auteur) Volcanoes are a potential hazard to over 750 million people worldwide. Accessing them to install monitoring equipment can be logistically challenging and dangerous. Traditional monitoring equipment is expensive and not available to many local communities. A new, low‐cost method is proposed to address these challenges using a unique 3D thermal photogrammetric modelling workflow. The data acquisition and processing part of this workflow has been tested on Stromboli, a volcano in the Aeolian Islands of Italy. Unmanned aerial vehicles (UAVs) were deployed at the volcano to acquire both visible and thermal infrared imagery. Both datasets were then digitally processed to produce 3D virtual outcrop models. Finally, the two datasets and models were integrated to produce the first 3D thermal photogrammetric model of an active volcano. The result is an easy‐to‐use workflow applicable to any volcano. This low‐cost monitoring system could be deployed in developing countries and remote areas otherwise hindered by limited budgets and poor access. Numéro de notice : A2019-578 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/phor.12301 date de publication en ligne : 23/12/2019 En ligne : https://doi.org/10.1111/phor.12301 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94464 [article]

A low‐cost open‐source workflow to generate georeferenced 3D SfM photogrammetric models of rocky outcrops / Laurent Froideval in Photogrammetric record, vol 34 n° 168 (December 2019)  

Public [article]  inPhotogrammetric record > vol 34 n° 168 (December 2019) . - pp 365 - 384 Titre : A low‐cost open‐source workflow to generate georeferenced 3D SfM photogrammetric models of rocky outcrops Type de document : Article/Communication Auteurs : Laurent Froideval, Auteur ; Kevin Pedoja, Auteur ; Franck Garestier, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 365 - 384 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes descripteurs IGN] falaise [Termes descripteurs IGN] front rocheux [Termes descripteurs IGN] géoréférencement [Termes descripteurs IGN] GNSS en mode différentiel [Termes descripteurs IGN] image de drone [Termes descripteurs IGN] logiciel libre [Termes descripteurs IGN] modélisation 3D [Termes descripteurs IGN] Normandie (région 2016) [Termes descripteurs IGN] Open MVG [Termes descripteurs IGN] point d'appui [Termes descripteurs IGN] structure-from-motion Résumé : (auteur) Structure‐from‐Motion (SfM) software and unmanned aerial vehicles (UAVs) have been increasingly adopted in the geosciences. The current mainstream generation of 3D models is still expensive as it relies on UAVs, Differential Global Navigation Satellite System (DGNSS) ground control points and commercial software. This paper proposes an end‐to‐end reproducible SfM workflow with minimal legal, financial or field issues. The procedure avoids both UAVs and DGNSS and relies on open‐source software. Interlocked models of a rocky shore in Normandy, France were generated at different scales and point densities, being georeferenced using free spatial data. A spherical target was used for scaling and assessing the relative accuracy, which was better than 1 cm. Numéro de notice : A2019-576 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/phor.12297 date de publication en ligne : 27/11/2019 En ligne : https://doi.org/10.1111/phor.12297 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94459 [article]

Matching of TerraSAR-X derived ground control points to optical image patches using deep learning / Tatjana Bürgmann in ISPRS Journal of photogrammetry and remote sensing, Vol 158 (December 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > Vol 158 (December 2019) . - pp 241 - 248 Titre : Matching of TerraSAR-X derived ground control points to optical image patches using deep learning Type de document : Article/Communication Auteurs : Tatjana Bürgmann, Auteur ; Wolfgang Koppe, Auteur ; Michael Schmitt, Auteur Année de publication : 2019 Article en page(s) : pp 241 - 248 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes descripteurs IGN] appariement d'images [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] géolocalisation [Termes descripteurs IGN] image multicapteur [Termes descripteurs IGN] image optique [Termes descripteurs IGN] image Pléiades [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] image TerraSAR-X [Termes descripteurs IGN] point d'appui Résumé : (auteur) High resolution synthetic aperture radar (SAR) satellites like TerraSAR-X are capable of acquiring images exhibiting an absolute geolocation accuracy within a few centimeters, mainly because of the availability of precise orbit information and by compensating range delay errors due to atmospheric conditions. In contrast, satellite images from optical missions generally exhibit comparably low geolocation accuracies because of the propagation of errors in angular measurements over large distances. However, a variety of remote sensing applications, such as change detection, surface movement monitoring or ice flow measurements, require precisely geo-referenced and co-registered satellite images. By using Ground Control Points (GCPs) derived from TerraSAR-X, the absolute geolocation accuracy of optical satellite images can be improved. For this purpose, the corresponding matching points in the optical images need to be localized. In this paper, a deep learning based approach is investigated for an automated matching of SAR-derived GCPs to optical image elements. Therefore, a convolutional neural network is pretrained with medium resolution Sentinel-1 and Sentinel-2 imagery and fine-tuned on precisely co-registered TerraSAR-X and Pléiades training image pairs to learn a common descriptor representation. By using these descriptors, the similarity of SAR and optical image patches can be calculated. This similarity metric is then used in a sliding window approach to identify the matching points in the optical reference image. Subsequently, the derived points can be utilized for co-registration of the underlying images. The network is evaluated over nine study areas showing airports and their rural surroundings from several different countries around the world. The results show that based on TerraSAR-X-derived GCPs, corresponding points in the optical image can automatically and reliably be identified with a pixel-level localization accuracy. Numéro de notice : A2019-548 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.09.010 date de publication en ligne : 05/11/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.09.010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94194 [article]

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