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Automatic detection of elevation changes by differential DSM analysis: application to urban areas / Cyrielle Guerin in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol 7 n° 10 (October 2014)  

Public [article]  inIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing > vol 7 n° 10 (October 2014) . - pp 4020 - 4037 Titre : Automatic detection of elevation changes by differential DSM analysis: application to urban areas Type de document : Article/Communication Auteurs : Cyrielle Guerin, Auteur ; Renaud Binet, Auteur ; Marc Pierrot-Deseilligny , Auteur Année de publication : 2014 Article en page(s) : pp 4020 - 4037 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] détection automatique [Termes IGN] détection de changement [Termes IGN] milieu urbain [Termes IGN] modèle numérique de surface [Termes IGN] qualité radiométrique (image) [Termes IGN] zone urbaine Résumé : (auteur) Research in change detection from optical satellite data is widely investigated as a support for visual image analysis. Most of the methods, however, are based on radiometric changes and are suffering from high false alarms rate due to irrelevant radiometric changes. Change detection based on the elevation difference between two dates, therefore, seems a good alternative to identify relevant changes, especially in a context of urban change detection. In the present work, we provide a fully automatic method of change detection based on a digital surface model (DSM) comparison. The processing flow includes the bundle block adjustment of all the available data as a preprocessing step, followed by an improved DSM generation scheme and a differential DSM analysis. The last two steps have been formulated as labeling problems and solved by an optimization method with a spatial regularization constraint. The solution of these labeling problems is computed with a generalized dynamic programming algorithm that is adapted according to the input data and the defined labels. The final DSMs reach a planimetric and altimetric resolution of about 1 m, allowing changes from 20nbspm2 to be detected. The results show that 33%-75% (respectively about 95%) of all changes (respectively, changes larger than 100nbspm2) are detected, depending on the employed regularization and the area. Moreover, the calculated kappa coefficient of the processing flow reaches up to 0.80, which emphasizes the method accuracy. All the above features lead to a significant gain compared to the classical visual image analysis. Numéro de notice : A2013-811 Affiliation des auteurs : ENSG+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/JSTARS.2014.2300509 Date de publication en ligne : 31/01/2014 En ligne : https://doi.org/10.1109/JSTARS.2014.2300509 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80096 [article]