Centre de documentation
scientifique

Tree position estimation from TLS data using hough transform and robust least-squares circle fitting / Maja Michałowska in Remote Sensing Applications: Society and Environment, RSASE, vol 29 (January 2023)  

Public [article]  inRemote Sensing Applications: Society and Environment, RSASE > vol 29 (January 2023) . - n° 100863 Titre : Tree position estimation from TLS data using hough transform and robust least-squares circle fitting Type de document : Article/Communication Auteurs : Maja Michałowska, Auteur ; Jacek Rapinski, Auteur ; Joanna Janicka, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 100863 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] branche (arbre) [Termes IGN] compensation par moindres carrés [Termes IGN] détection d'arbres [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] filtrage du bruit [Termes IGN] géolocalisation [Termes IGN] méthode robuste [Termes IGN] modèle numérique de terrain [Termes IGN] Pologne [Termes IGN] semis de points [Termes IGN] transformation de Hough Résumé : (auteur) Forest management and planning require information regarding the current state of the forest. Remote sensing techniques allow to obtain geospatial data, also for the forestry sector. As one of the remote-sensed technologies datasets, Terrestrial Laser Scanning data is widely used to derive detailed information about tree and forest stand parameters. This article presents the combination of circular Hough transform, denoising procedure, and robust least-square circle fitting method to extract stem positions from Terrestrial Laser Scanning data. In the proposed approach, initial tree stems position was detected with circular Hough transform. Then, obtained results were denoised to exclude most non-tree trunk points and analyze three-dimensional data from laser scanning to find exact circular tree stems with a robust least-square circle fitting method. The developed algorithm is effective in obtaining the trees’ geodetic positions from laser scanning data. The results generated in this study can be used as basics for further automatic determination of tree characteristics, such as tree species, height, or crown range. In this study, 94.8% tree stems delineation was generated with a mean accuracy of 87.2%, 1.64 cm of root mean square error for stem position, and 1.15 cm for tree radius measured at ground level. The process conducted in this research can be used to detect other circle-shaped objects, such as lamps or power towers, for which obtaining dense Terrestrial Laser Scanning data is available. The detected positions of these objects can power the geographic information systems or thematic industry systems, where it is necessary to determine the geodetic object position results from legal regulations. Numéro de notice : A2023-018 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rsase.2022.100863 Date de publication en ligne : 04/11/2022 En ligne : https://doi.org/10.1016/j.rsase.2022.100863 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102183 [article]

Line-based deep learning method for tree branch detection from digital images / Rodrigo L. S. Silva in International journal of applied Earth observation and geoinformation, vol 110 (June 2022)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 110 (June 2022) . - n° 102759 Titre : Line-based deep learning method for tree branch detection from digital images Type de document : Article/Communication Auteurs : Rodrigo L. S. Silva, Auteur ; José Marcato Junior, Auteur ; Laisa Almeida, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 102759 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] branche (arbre) [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection d'objet [Termes IGN] données qualitatives [Termes IGN] estimation quantitative [Termes IGN] image à haute résolution [Termes IGN] ligne (géométrie) [Termes IGN] transformation de Hough Résumé : (auteur) Preventive maintenance of power lines, including cutting and pruning of tree branches, is essential to avoid interruptions in the energy supply. Automatic methods can support this risky task and also reduce time-consuming. Here, we propose a method in which the orientation and the grasping positions of tree branches are estimated. The proposed method firstly predicts the straight line (representing the tree branch extension) based on a convolutional neural network (CNN). Secondly, a Hough transform is applied to estimate the direction and position of the line. Finally, we estimate the grip point as the pixel point with the highest probability of belonging to the line. We generated a dataset based on internet searches and annotated 1868 images considering challenging scenarios with different tree branch shapes, capture devices, and environmental conditions. Ten-fold cross-validation was adopted, considering 90% for training and 10% for testing. We also assessed the method under corruptions (gaussian and shot) with different severity levels. The experimental analysis showed the effectiveness of the proposed method reporting F1-score of 96.78%. Our method outperformed state-of-the-art Deep Hough Transform (DHT) and Fully Convolutional Line Parsing (F-Clip). Numéro de notice : A2022-550 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.jag.2022.102759 Date de publication en ligne : 09/05/2022 En ligne : https://doi.org/10.1016/j.jag.2022.102759 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101153 [article]

Semi-automatic reconstruction of object lines using a smartphone’s dual camera / Mohammed Aldelgawy in Photogrammetric record, Vol 36 n° 176 (December 2021)  

Public [article]  inPhotogrammetric record > Vol 36 n° 176 (December 2021) . - pp 381 - 401 Titre : Semi-automatic reconstruction of object lines using a smartphone’s dual camera Type de document : Article/Communication Auteurs : Mohammed Aldelgawy, Auteur ; Isam Abu-Qasmieh, Auteur Année de publication : 2021 Article en page(s) : pp 381 - 401 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] acquisition d'images [Termes IGN] appariement d'images [Termes IGN] chambre non métrique [Termes IGN] correction d'image [Termes IGN] étalonnage [Termes IGN] extraction de traits caractéristiques [Termes IGN] forme linéaire [Termes IGN] intersection spatiale [Termes IGN] objectif grand angulaire [Termes IGN] reconstruction d'image [Termes IGN] téléphone intelligent [Termes IGN] transformation de Hough Résumé : (Auteur) In this paper, the possibility of reconstructing object lines using a smartphone’s rear dual camera (wide-angle and telephoto) was examined through designing a semi-automatic system. After calibrating both cameras, six scenes for each of three objects were captured and rectified. Object lines were categorised into six groups based on the distance and angle to the dual camera system. Image lines were extracted using the linear Hough transform technique and points of intersection detected. Stereo pairing of conjugate points then allowed the calculation of object coordinates and the lengths of object lines were compared to their lengths measured by a digital caliper. The best line reconstruction results were achieved with the smallest distance and angle to the dual camera system. Numéro de notice : A2021-915 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/phor.12388 Date de publication en ligne : 19/10/2021 En ligne : https://doi.org/10.1111/phor.12388 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99330 [article]

Flood depth mapping in street photos with image processing and deep neural networks / Bahareh Alizadeh Kharazi in Computers, Environment and Urban Systems, vol 88 (July 2021)  

Public [article]  inComputers, Environment and Urban Systems > vol 88 (July 2021) . - n° 101628 Titre : Flood depth mapping in street photos with image processing and deep neural networks Type de document : Article/Communication Auteurs : Bahareh Alizadeh Kharazi, Auteur ; Amir H. Behzadan, Auteur Année de publication : 2021 Article en page(s) : n° 101628 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage profond [Termes IGN] Canada [Termes IGN] centre urbain [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] crue [Termes IGN] détection de contours [Termes IGN] Etats-Unis [Termes IGN] image Streetview [Termes IGN] inondation [Termes IGN] profondeur [Termes IGN] signalisation routière [Termes IGN] système d'aide à la décision [Termes IGN] traitement d'image [Termes IGN] transformation de Hough [Termes IGN] zone urbaine Résumé : (auteur) Many parts of the world experience severe episodes of flooding every year. In addition to the high cost of mitigation and damage to property, floods make roads impassable and hamper community evacuation, movement of goods and services, and rescue missions. Knowing the depth of floodwater is critical to the success of response and recovery operations that follow. However, flood mapping especially in urban areas using traditional methods such as remote sensing and digital elevation models (DEMs) yields large errors due to reshaped surface topography and microtopographic variations combined with vegetation bias. This paper presents a deep neural network approach to detect submerged stop signs in photos taken from flooded roads and intersections, coupled with Canny edge detection and probabilistic Hough transform to calculate pole length and estimate floodwater depth. Additionally, a tilt correction technique is implemented to address the problem of sideways tilt in visual analysis of submerged stop signs. An in-house dataset, named BluPix 2020.1 consisting of paired web-mined photos of submerged stop signs across 10 FEMA regions (for U.S. locations) and Canada is used to evaluate the models. Overall, pole length is estimated with an RMSE of 17.43 and 8.61 in. in pre- and post-flood photos, respectively, leading to a mean absolute error of 12.63 in. in floodwater depth estimation. Findings of this research are sought to equip jurisdictions, local governments, and citizens in flood-prone regions with a simple, reliable, and scalable solution that can provide (near-) real time estimation of floodwater depth in their surroundings. Numéro de notice : A2021-358 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101628 Date de publication en ligne : 01/04/2021 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101628 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97620 [article]

Reconnaissance automatique d’objets pour le jumeau numérique ferroviaire à partir d’imagerie aérienne / Valentin Desbiolles in XYZ, n° 167 (juin 2021)

Public [article]  inXYZ > n° 167 (juin 2021) . - pp 33 - 38 Titre : Reconnaissance automatique d’objets pour le jumeau numérique ferroviaire à partir d’imagerie aérienne Type de document : Article/Communication Auteurs : Valentin Desbiolles, Auteur Année de publication : 2021 Article en page(s) : pp 33 - 38 Note générale : Bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] Autocad Map [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] dessin assisté par ordinateur [Termes IGN] détection automatique [Termes IGN] détection d'objet [Termes IGN] image aérienne [Termes IGN] jumeau numérique [Termes IGN] orthoimage [Termes IGN] reconnaissance d'objets [Termes IGN] transformation de Hough [Termes IGN] voie ferrée Résumé : (Auteur) Ce projet propose une étude sur l’insertion automatique d’objets utiles au fonctionnement d’une voie ferrée dans un plan DAO. Ces objets sont visibles sur des orthophotos acquises par moyens aéroportés (drone ou hélicoptère). La solution se scinde en deux grands axes : 1- la détection et la localisation des objets d’intérêt sur une orthophoto ; 2- leurs insertions dans un plan DAO. Ce PFE parcourt ainsi les différentes techniques pour automatiser une phase de reconnaissance de certains éléments cibles sur une image pour finir sur le développement d’une méthode permettant de les reporter dans un plan DAO automatiquement. Numéro de notice : A2021-462 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueNat DOI : sans Date de publication en ligne : 01/06/2021 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97928 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
112-2021021	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Automated street tree inventory using mobile LiDAR point clouds based on Hough transform and active contours / Amir Hossein Safaie in ISPRS Journal of photogrammetry and remote sensing, vol 174 (April 2021)  

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Automatic object extraction from airborne laser scanning point clouds for digital base map production / Elyta Widyaningrum (2021)  

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Improving traffic sign recognition results in urban areas by overcoming the impact of scale and rotation / Roholah Yazdan in ISPRS Journal of photogrammetry and remote sensing, vol 171 (January 2021)  

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Crater detection and registration of planetary images through marked point processes, multiscale decomposition, and region-based analysis / David Solarna in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)  

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Planar polygons detection in lidar scans based on sensor topology enhanced Ransac / Stéphane Guinard in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2020 (August 2020)  

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A point cloud feature regularization method by fusing judge criterion of field force / Xijiang Chen in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)  

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Automated extraction of lane markings from mobile LiDAR point clouds based on fuzzy inference / Heidar Rastiveis in ISPRS Journal of photogrammetry and remote sensing, vol 160 (February 2020)  

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Acquisition par drone et le récolement en VRD / Florent Authié (2020)  

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Comparison of three algorithms to estimate tree stem diameter from terrestrial laser scanner data / Joris Ravaglia in Forests, vol 10 n° 7 (July 2019)  

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Mise en place d’un processus de dessin automatisé de plans d’intérieurs à partir de nuages de points acquis par LIDAR / Léa Talec (2017) 

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