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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)  

Public [article]  inISPRS Open Journal of Photogrammetry and Remote Sensing > vol 5 (August 2022) 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 stochastique Résumé : (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 [article]