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Radiometric calibration assessments for UAS-borne multispectral cameras: Laboratory and field protocols / Sen Cao in ISPRS Journal of photogrammetry and remote sensing, vol 149 (March 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 149 (March 2019) . - pp 132 - 145 Titre : Radiometric calibration assessments for UAS-borne multispectral cameras: Laboratory and field protocols Type de document : Article/Communication Auteurs : Sen Cao, Auteur ; Brad Danielson, Auteur ; Shari Clare, Auteur Année de publication : 2019 Article en page(s) : pp 132 - 145 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s) [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] bande infrarouge [Termes descripteurs IGN] bande rouge [Termes descripteurs IGN] capteur multibande [Termes descripteurs IGN] drone [Termes descripteurs IGN] étalonnage radiométrique [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] réflectance spectrale [Termes descripteurs IGN] test de performance Résumé : (Auteur) The main objective of this study was to develop and test a framework that can be used by Unmanned Aerial Systems (UAS) operators with varying technical backgrounds to estimate the accuracy and reliability of multispectral (visible and Near-Infrared or NIR) sensor measurements. We evaluated the performance of two multispectral sensors – the MicaSense RedEdge and the Airinov MultiSpec 4C – in both a laboratory and field setting. In the laboratory, we measured the reflectance of a number of reference target materials using each UAS sensor, and compared the values to those measured using a calibrated spectrometer. We found a strong linear relationship between the measurements made by the MicaSense RedEdge and the spectrometer, while the relationship was much weaker for the Airinov MultiSpec 4C, particularly in the longer wavelength bands (red-edge and NIR). A sub-set of the target materials were selected as ground reference targets for three field calibration exercises. In field calibration assessment No. 1, imagery was collected using each UAS sensor and reflectance values were extracted from pixels covering the ground reference targets. The extracted values were compared to the reflectance values acquired in the laboratory, and both UAS sensors were found to over-estimate reflectance, with lower accuracy in red-edge and NIR bands. Field calibration assessment No. 2 involved a calculation of Normalized Difference Vegetation Index (NDVI) values at field control points using both UAS sensors, and we found a strong linear relationship between the NDVI values and measurements made by a hand-held NDVI sensor, suggesting that the calculation of a normalized band ratio (i.e., NDVI) effectively reduces the reflectance measurement inaccuracy that we observed previously. Field calibration assessment No. 3 included image acquisition of ground reference targets using the MicaSense RedEdge sensor over seventeen sequential field surveys. Results revealed measurement variability over time, suggesting that daily differences in solar illumination and atmospheric conditions may influence derived reflectance values. In light of these results, we propose simplified procedures that can be adopted by UAS operators to periodically assess the radiometric fidelity of their multispectral sensors. Numéro de notice : A2019-226 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.01.016 date de publication en ligne : 29/01/2019 En ligne : https://doi.org/ Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92445 [article]

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UAS lidar for ecological restoration of wetlands / Marie de Boisvilliers in GIM international, Vol 33 n° 2 (March - April 2019)  

Public [article]  inGIM international > Vol 33 n° 2 (March - April 2019) . - pp 29 - 31 Titre : UAS lidar for ecological restoration of wetlands Type de document : Article/Communication Auteurs : Marie de Boisvilliers, Auteur ; Morgane Selve, Auteur Année de publication : 2019 Article en page(s) : pp 29 - 31 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] drone [Termes descripteurs IGN] eau de surface [Termes descripteurs IGN] écosystème [Termes descripteurs IGN] marais [Termes descripteurs IGN] modèle numérique de terrain [Termes descripteurs IGN] QGIS [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] Terrascan [Termes descripteurs IGN] zone humide Résumé : (Auteur) Wetlands are essential ecosystems which provide numerous benefits to society as a whole. But their functionality strongly depends on the hydrology and topography of the watershed, thus creating the need for monitoring. The use of terrestrial topographical survey methods can be a challenging task in wetlands, however. Flooded areas, muddy terrain and low vegetation can substantially slow down or even prevent the movement of surveyors, while tall vegetation can obstruct GPS reception. As this article outlines, advanced technologies such as airborne or UAS Lidar offer interesting alternatives for surveying the hydrology and topography of wetlands. Numéro de notice : A2019-162 Thématique : BIODIVERSITE/IMAGERIE Nature : Article DOI : sans En ligne : https://www.gim-international.com/content/article/uas-lidar-for-ecological-resto [...] Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92567 [article]

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Estimating forest structural attributes using UAV-LiDAR data in Ginkgo plantations / Kun Liu in ISPRS Journal of photogrammetry and remote sensing, vol 146 (December 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 146 (December 2018) . - pp 465 - 482 Titre : Estimating forest structural attributes using UAV-LiDAR data in Ginkgo plantations Type de document : Article/Communication Auteurs : Kun Liu, Auteur ; Xin Shen, Auteur ; Lin Cao, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 465 - 482 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] analyse de sensibilité [Termes descripteurs IGN] biomasse aérienne [Termes descripteurs IGN] canopée [Termes descripteurs IGN] Chine [Termes descripteurs IGN] détection d'arbres [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] drone [Termes descripteurs IGN] échelle des données [Termes descripteurs IGN] erreur moyenne quadratique [Termes descripteurs IGN] Ginkgo biloba [Termes descripteurs IGN] plantation forestière [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] structure de la végétation Résumé : (auteur) Estimating forest structural attributes in planted forests is crucial for sustainably management of forests and helps to understand the contributions of forests to global carbon storage. The Unmanned Aerial Vehicle-Light Detecting and Ranging (UAV-LiDAR) has become a promising technology and attempts to be used for forest management, due to its capacity to provide highly accurate estimations of three-dimensional (3D) forest structural information with a lower cost, higher flexibility and finer resolution than airborne LiDAR. In this study, the effectiveness of plot-level metrics (i.e., distributional, canopy volume and Weibull-fitted metrics) and individual-tree-summarized metrics (i.e., maximum, minimum and mean height of trees and the number of trees from the individual tree detection (ITD) results) derived from UAV-LiDAR point clouds were assessed, then these metrics were used to fit estimation models of six forest structural attributes by parametric (i.e., partial least squares (PLS)) and non-parametric (i.e., k-Nearest Neighbors (k-NN) and Random Forest (RF)) approaches, within a Ginkgo plantation in east China. In addition, we assessed the effects of UAV-LiDAR point cloud density on the derived metrics and individual tree segmentation results, and evaluated the correlations of these metrics with aboveground biomass (AGB) by a sensitivity analysis. The results showed that, in general, models based on both plot-level and individual-tree-summarized metrics (CV-R2 = 0.66–0.97, rRMSE = 2.83–23.35%) performed better than models based on the plot-level metrics only (CV-R2 = 0.62–0.97, rRMSE = 3.81–27.64%). PLS had a relatively high prediction accuracy for Lorey’s mean height (CV-R2 = 0.97, rRMSE = 2.83%), whereas k-NN performed well for predicting volume (CV-R2 = 0.94, rRMSE = 8.95%) and AGB (CV-R2 = 0.95, rRMSE = 8.81%). For the point cloud density sensitivity analysis, the canopy volume metrics showed a higher dependence on point cloud density than other metrics. ITD results showed a relatively high accuracy (F1-score > 74.93%) when the point cloud density was higher than 10% (16 pts·m−2). The correlations between AGB and the metrics of height percentiles, lower height level of canopy return densities and canopy cover appeared stable across different point cloud densities when the point cloud density was reduced from 50% (80 pts·m−2) to 5% (8 pts·m−2). Numéro de notice : A2018-544 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.11.001 date de publication en ligne : 08/11/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.11.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91570 [article]

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GNSS-assisted integrated sensor orientation with sensor pre-calibration for accurate corridor mapping / Yilin Zhou in Sensors, vol 18 n° 9 (September 2018)  

Public [article]  inSensors > vol 18 n° 9 (September 2018) Titre : GNSS-assisted integrated sensor orientation with sensor pre-calibration for accurate corridor mapping Type de document : Article/Communication Auteurs : Yilin Zhou, Auteur ; Ewelina Rupnik , Auteur ; Paul-Henri Faure, Auteur ; Marc Pierrot-Deseilligny , Auteur Année de publication : 2018 Projets : 1-Pas de projet / Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes descripteurs IGN] chambre métrique [Termes descripteurs IGN] compensation par faisceaux [Termes descripteurs IGN] drone [Termes descripteurs IGN] modèle géométrique de prise de vue [Termes descripteurs IGN] orientation du capteur intégrée [Termes descripteurs IGN] point d'appui [Termes descripteurs IGN] récepteur GNSS Résumé : (auteur) With the development of unmanned aerial vehicles (UAVs) and global navigation satellite system (GNSS), the accurate camera positions at exposure can be known and the GNSS-assisted bundle block adjustment (BBA) approach is possible for integrated sensor orientation (ISO). This study employed ISO approach for camera pose determination with the objective of investigating the impact of a good sensor pre-calibration on a poor acquisition geometry. Within the presented works, several flights were conducted on a dike by a small UAV embedded with a metric camera and a GNSS receiver. The multi-lever-arm estimation within the BBA procedure makes it possible to merge image blocks of different configurations such as nadir and oblique images without physical constraints on camera and GNSS antenna positions. The merged image block achieves a better accuracy and the sensor self-calibrated well. The issued sensor calibration is then applied to a less preferable acquisition configuration and the accuracy is significantly improved. For a corridor acquisition scene of about 600 m , a centimetric accuracy is reached with one GCP. With the provided sensor pre-calibration, an accuracy of 3.9 cm is achieved without any GCP. Numéro de notice : A2018-388 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/s18092783 date de publication en ligne : 24/08/2018 En ligne : https://doi.org/10.3390/s18092783 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90803 [article]

A deep learning approach to DTM extraction from imagery using rule-based training labels / Caroline M. Gevaert in ISPRS Journal of photogrammetry and remote sensing, vol 142 (August 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 142 (August 2018) . - pp 106 - 123 Titre : A deep learning approach to DTM extraction from imagery using rule-based training labels Type de document : Article/Communication Auteurs : Caroline M. Gevaert, Auteur ; Claudio Persello, Auteur ; George Vosselman, Auteur Année de publication : 2018 Article en page(s) : pp 106 - 123 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] base de règles [Termes descripteurs IGN] benchmark spatial [Termes descripteurs IGN] Dar-es-Salam (Tanzanie) [Termes descripteurs IGN] drone [Termes descripteurs IGN] échantillonnage d'image [Termes descripteurs IGN] extraction automatique [Termes descripteurs IGN] Kigali (Rwanda) [Termes descripteurs IGN] Lombardie [Termes descripteurs IGN] modèle numérique de terrain [Termes descripteurs IGN] photogrammétrie aérienne [Termes descripteurs IGN] réseau neuronal convolutif Résumé : (Auteur) Existing algorithms for Digital Terrain Model (DTM) extraction still face difficulties due to data outliers and geometric ambiguities in the scene such as contiguous off-ground areas or sloped environments. We postulate that in such challenging cases, the radiometric information contained in aerial imagery may be leveraged to distinguish between ground and off-ground objects. We propose a method for DTM extraction from imagery which first applies morphological filters to the Digital Surface Model to obtain candidate ground and off-ground training samples. These samples are used to train a Fully Convolutional Network (FCN) in the second step, which can then be used to identify ground samples for the entire dataset. The proposed method harnesses the power of state-of-the-art deep learning methods, while showing how they can be adapted to the application of DTM extraction by (i) automatically selecting and labelling dataset-specific samples which can be used to train the network, and (ii) adapting the network architecture to consider a larger surface area without unnecessarily increasing the computational burden. The method is successfully tested on four datasets, indicating that the automatic labelling strategy can achieve an accuracy which is comparable to the use of manually labelled training samples. Furthermore, we demonstrate that the proposed method outperforms two reference DTM extraction algorithms in challenging areas. Numéro de notice : A2018-298 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.06.001 date de publication en ligne : 15/06/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.06.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90410 [article]

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Detecting newly grown tree leaves from unmanned-aerial-vehicle images using hyperspectral target detection techniques / Chinsu Lin in ISPRS Journal of photogrammetry and remote sensing, vol 142 (August 2018)  

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Drones et SIG / Anonyme in Géomatique expert, n° 122 (mai-juin 2018)

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Extracting leaf area index using viewing geometry effects : A new perspective on high-resolution unmanned aerial system photography / Lukas Roth in ISPRS Journal of photogrammetry and remote sensing, vol 141 (July 2018)  

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Deep convolutional neural network training enrichment using multi-view object-based analysis of Unmanned Aerial systems imagery for wetlands classification / Tao Liu in ISPRS Journal of photogrammetry and remote sensing, vol 139 (May 2018)  

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Entre l’écorce et l’arbre / Jean Hamann in Le Fil : Le journal de la communauté universitaire, vol 53 n° 25 ([19/04/2018])  

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A novel orthoimage mosaic method using a weighted A∗ algorithm : Implementation and evaluation / Maoteng Zheng in ISPRS Journal of photogrammetry and remote sensing, vol 138 (April 2018)  

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Analyse du risque végétation dans les emprises ferroviaires à partir de données LiDAR acquises par drones / Luc Perrin in XYZ, n° 154 (mars - mai 2018)

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Image classification-based ground filtering of point clouds extracted from UAV-based aerial photos / Volkan Yilmaz in Geocarto international, vol 33 n° 3 (March 2018)  

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Littoral, "Ricochet" ausculte / Marielle Mayo in Géomètre, n° 2155 (février 2018)

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Egalisation radiométrique d'orthoimages thermiques / Jonathan Lisein (2018)    

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