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Generating impact maps from bomb craters automatically detected in aerial wartime images using marked point processes / Christian Kruse in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 5 (August 2022)
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Titre : Generating impact maps from bomb craters automatically detected in aerial wartime images using marked point processes Type de document : Article/Communication Auteurs : Christian Kruse, Auteur ; Dennis Wittich, Auteur ; Franz Rottensteiner, Auteur ; et al., Auteur Année de publication : 2022 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme du recuit simulé
[Termes IGN] chevauchement
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection automatique
[Termes IGN] échantillonnage de données
[Termes IGN] Europe centrale
[Termes IGN] guerre
[Termes IGN] image aérienne
[Termes IGN] méthode de Monte-Carlo par chaînes de Markov
[Termes IGN] méthode fondée sur le noyau
[Termes IGN] processus ponctuel marqué
[Termes IGN] processus stochastiqueRésumé : (auteur) Even more than 75 years after the Second World War, numerous unexploded bombs (duds) linger in the ground and pose a considerable hazard to society. The areas containing these duds are documented in so-called impact maps, which are based on locations of exploded bombs; these locations can be found in aerial images taken shortly after bombing. To generate impact maps, in this paper we present a novel approach based on marked point processes (MPPs) for the automatic detection of bomb craters in such images, some of which are overlapping. The object model for the craters is represented by circles and is embedded in the MPP-framework. By means of stochastic sampling, the most likely configuration of objects within the scene is determined. Each configuration is evaluated using an energy function that describes the consistency with a predefined object model. High gradient magnitudes along the object borders and homogeneous grey values inside the objects are favoured, while overlaps between objects are penalized. Reversible Jump Markov Chain Monte Carlo sampling, in combination with simulated annealing, provides the global optimum of the energy function. Our procedure allows the combination of individual detection results covering the same location. Afterwards, a probability map for duds is generated from the detections via kernel density estimation and areas around the detections are classified as contaminated, resulting in an impact map. Our results, based on 74 aerial wartime images taken over different areas in Central Europe, show the potential of the method; among other findings, a clear improvement is achieved by using redundant image information. We also compared the MPP method for bomb crater detection with a state-of-of-the-art convolutional neural network (CNN) for generating region proposals; it turned out that the CNN outperforms the MPPs if a sufficient amount of representative training data is available and a threshold for a region to be considered as crater is properly tuned prior to running the experiments. If this is not the case, the MPP approach achieves better results. Numéro de notice : A2022-515 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.ophoto.2022.100017 Date de publication en ligne : 02/06/2022 En ligne : https://doi.org/10.1016/j.ophoto.2022.100017 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101057
in ISPRS Open Journal of Photogrammetry and Remote Sensing > vol 5 (August 2022)[article]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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Titre : Crater detection and registration of planetary images through marked point processes, multiscale decomposition, and region-based analysis Type de document : Article/Communication Auteurs : David Solarna, Auteur ; Alberto Gotelli, Auteur ; Jacqueline Le Moigne, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 6039 - 6058 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] cratère
[Termes IGN] détection de contours
[Termes IGN] distance de Hausdorff
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image multitemporelle
[Termes IGN] image thermique
[Termes IGN] Mars (planète)
[Termes IGN] ondelette
[Termes IGN] processus ponctuel marqué
[Termes IGN] séparateur à vaste marge
[Termes IGN] transformation de Hough
[Termes IGN] zone d'intérêtRésumé : (auteur) Because of the large variety of planetary sensors and spacecraft already collecting data and with many new and improved sensors being planned for future missions, planetary science needs to integrate numerous multimodal image sources, and, as a consequence, accurate and robust registration algorithms are required. In this article, we develop a new framework for crater detection based on marked point processes (MPPs) that can be used for planetary image registration. MPPs were found to be effective for various object detection tasks in Earth observation, and a new MPP model is proposed here for detecting craters in planetary data. The resulting spatial features are exploited for registration, together with fitness functions based on the MPP energy, on the mean directed Hausdorff distance, and on the mutual information. Two different methods—one based on birth–death processes and region-of-interest analysis and the other based on graph cuts and decimated wavelets—are developed within the proposed framework. Experiments with a large set of images, including 13 thermal infrared and visible images of the Mars surface, 20 semisimulated multitemporal pairs of images of the Mars surface, and a real multitemporal image pair of the Lunar surface, demonstrate the effectiveness of the proposed framework in terms of crater detection performance as well as for subpixel registration accuracy. Numéro de notice : A2020-526 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2970908 Date de publication en ligne : 18/03/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2970908 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95704
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 9 (September 2020) . - pp 6039 - 6058[article]Detection of individual trees in urban alignment from airborne data and contextual information: A marked point process approach / Josselin Aval in ISPRS Journal of photogrammetry and remote sensing, vol 146 (December 2018)
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Titre : Detection of individual trees in urban alignment from airborne data and contextual information: A marked point process approach Type de document : Article/Communication Auteurs : Josselin Aval, Auteur ; Jean Demuynck, Auteur ; Emmanuel Zenou, Auteur ; Sophie Fabre, Auteur ; David Sheeren , Auteur ; Mathieu Fauvel, Auteur ; Karine R.M. Adeline, Auteur ; Xavier Briottet
, Auteur
Année de publication : 2018 Article en page(s) : pp 197 - 210 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] arbre urbain
[Termes IGN] canopée
[Termes IGN] détection d'arbres
[Termes IGN] données lidar
[Termes IGN] hauteur des arbres
[Termes IGN] houppier
[Termes IGN] image hyperspectrale
[Termes IGN] modèle numérique de surface
[Termes IGN] prise en compte du contexte
[Termes IGN] processus ponctuel marqué
[Termes IGN] système d'information géographique
[Termes IGN] Toulouse
[Termes IGN] zone urbaineRésumé : (Auteur) With the current expansion of cities, urban trees have an important role for preserving the health of its inhabitants. With their evapotranspiration, they reduce the urban heat island phenomenon, by trapping CO2 emission, improve air quality. In particular, street trees or alignment trees, create shade on the road network, are structuring elements of the cities and decorate the roads. Street trees are also subject to specific conditions as they have little space for growth, are pruned and can be affected by the spread of diseases in single-species plantations. Thus, their detection, identification and monitoring are necessary. In this study, an approach is proposed for mapping these trees that are characteristic of the urban environment. Three areas of the city of Toulouse in the south of France are studied. Airborne hyperspectral data and a Digital Surface Model (DSM) for high vegetation detection are used. Then, contextual information is used to identify the street trees. Indeed, Geographic Information System (GIS) data are considered to detect the vegetation canopies close to the streets. Afterwards, individual street tree crown delineation is carried out by modeling the discriminative contextual features of individual street trees (hypotheses of small angle between the trees and similar heights) based on Marked Point Process (MPP). Compared to a baseline individual tree crown delineation method based on region growing, our method logically provides the best results with F-score values of 91%, 75% and 85% against 70%, 41% and 20% for the three studied areas respectively. Our approach mainly succeeds in identifying the street trees. In addition, the contribution of the angle, the height and the GIS data in the street tree mapping has been studied. The results encourage the use of the angle, the height and the GIS data together. However, with only the angle and the height, the results are similar to those obtained with the inclusion of the GIS data for the first and the second study cases with F-score values of 88%, 79% and 62% against 91%, 75% and 85% for the three study cases respectively. Finally, it is shown that the GIS data only is not sufficient. Numéro de notice : A2018-538 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.09.016 Date de publication en ligne : 21/10/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.09.016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91552
in ISPRS Journal of photogrammetry and remote sensing > vol 146 (December 2018) . - pp 197 - 210[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2018131 RAB Revue Centre de documentation En réserve L003 Disponible 081-2018133 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2018132 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt Large scale road network extraction in forested moutainous areas using airborne laser scanning data / António Ferraz (2014)
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Titre : Large scale road network extraction in forested moutainous areas using airborne laser scanning data Type de document : Article/Communication Auteurs : António Ferraz , Auteur ; Clément Mallet
, Auteur ; Nesrine Chehata
, Auteur
Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2014 Conférence : IGARSS 2014, International Geoscience And Remote Sensing Symposium 13/07/2014 18/07/2014 Québec Québec - Canada Proceedings IEEE Importance : pp 4315 - 4318 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage automatique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] extraction du réseau routier
[Termes IGN] forêt alpestre
[Termes IGN] France (administrative)
[Termes IGN] montagne
[Termes IGN] processus ponctuel marqué
[Termes IGN] reconnaissance de formes
[Termes IGN] théorie des graphesRésumé : (auteur) In this work, we present an approach that is able to deal with large-scale road network mapping. While former methods focus on delineating patches of roads without computing a coherent road network, we formulate a very large number of road hypothesis that are pruned using a graph reasoning and weak a priori knowledge on road behavior. The initial solution is computed by means of two machine learning and pattern recognition state-of-the-art methods (namely, Random Forest classification and Marked Point Process) that allow to process very large areas in little time with very satisfactory results. Numéro de notice : C2014-024 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/IGARSS.2014.6947444 Date de publication en ligne : 06/11/2014 En ligne : http://dx.doi.org/10.1109/IGARSS.2014.6947444 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92029 Documents numériques
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contenu dans Proceedings, Commission 3, XXII ISPRS Congress, 25 August – 01 September 2012, Melbourne, Australia / M. Shortis (2012)
Titre : LIBRJMCMC: an open-source generic C++ library for stochastic optimization Type de document : Article/Communication Auteurs : Mathieu Brédif , Auteur ; Olivier Tournaire
, Auteur
Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2012 Collection : International Archives of Photogrammetry and Remote Sensing, ISSN 0252-8231 num. 39-B3 Conférence : ISPRS 2012, Commission 3, 22th international congress 25/08/2012 01/09/2012 Melbourne Australie OA ISPRS Archives Commission 3 Importance : pp 259 - 264 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Produits informatiques
[Termes IGN] algorithme du recuit simulé
[Termes IGN] algorithme RJMCMC
[Termes IGN] bibliothèque logicielle
[Termes IGN] code source libre
[Termes IGN] processus ponctuel marqué
[Termes IGN] programmation stochastique
[Termes IGN] semis de pointsRésumé : (auteur) The LIBRJMCMC is an open source C++ library that solves optimization problems using a stochastic framework. The library is primarily intended for but not limited to research purposes in computer vision, photogrammetry and remote sensing, as it has initially been developed in the context of extracting building footprints from digital elevation models using a marked point process of rectangles. It has been designed to be both highly modular and extensible, and have computational times comparable to a code specifically designed for a particular application, thanks to the powerful paradigms of metaprogramming and generic programming. The proposed stochastic optimization is built on the coupling of a stochastic Reversible-Jump Markov Chain Monte Carlo (RJMCMC) sampler and a simulated annealing relaxation. This framework allows, with theoretical guarantees, the optimization of an unrestricted objective function without requiring any initial solution.
The modularity of our library allows the processing of any kind of input data, whether they are 1D signals (e.g. LiDAR or SAR waveforms), 2D images, 3D point clouds... The library user has just to define a few modules describing its domain specific context: the encoding of a configuration (e.g. its object type in a marked point process context), reversible jump kernels (e.g. birth, death, modifications...), the optimized energies (e.g. data and regularization terms) and the probabilized search space given by the reference process. Similar to this extensibility in the application domain, concepts are clearly and orthogonally separated such that it is straightforward to customize the convergence test, the temperature schedule, or to add visitors enabling visual feedback during the optimization. The library offers dedicated modules for marked point processes, allowing the user to optimize a Maximum A Posteriori (MAP) criterion with an image data term energy on a marked point process of rectangles.Numéro de notice : C2012-014 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/isprsarchives-XXXIX-B3-23-2012 En ligne : http://dx.doi.org/10.5194/isprsarchives-XXXIX-B3-259-2012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94277 Approche non supervisée par processus ponctuels marqués pour l'extraction d'objets à partir d'images aériennes et satellitaires / S. Ben Hadj in Revue Française de Photogrammétrie et de Télédétection, n° 194 (Mai 2011)
PermalinkGeometric feature extraction by a multimarked point process / Florent Lafarge in IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI, vol 32 n° 9 (September 2010)
PermalinkPermalinkA geometric stochastic approach based on marked point processes for road mark detection from high resolution aerial images / Olivier Tournaire in ISPRS Journal of photogrammetry and remote sensing, vol 64 n° 6 (November - December 2009)
PermalinkUse Markov random fields for automatic cloud-shadow detection on high resolution / Sylvie Le Hégarat-Mascle in ISPRS Journal of photogrammetry and remote sensing, vol 64 n° 4 (July - August 2009)
PermalinkAutomatic building extraction from DEMs using an object approach and application to the 3D-city modeling / Florent Lafarge in ISPRS Journal of photogrammetry and remote sensing, vol 63 n° 3 (May - June 2008)
PermalinkPermalinkAutomatic 3D building reconstruction from DEMs: an application to Pleiades simulations / Florent Lafarge in Revue Française de Photogrammétrie et de Télédétection, n° 184 (Décembre 2006)
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