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Comparison of belief propagation and graph-cut approaches for contextual classification of 3D LIDAR point cloud data / Loïc Landrieu (2017)    

Public Titre : Comparison of belief propagation and graph-cut approaches for contextual classification of 3D LIDAR point cloud data Type de document : Article/Communication Auteurs : Loïc Landrieu , Auteur ; Clément Mallet , Auteur ; Martin Weinmann, Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2017 Autre Editeur : Saint-Mandé : Institut national de l'information géographique et forestière - IGN (2012-) Projets : 1-Pas de projet / Conférence : IGARSS 2017, IEEE International Geoscience And Remote Sensing Symposium 23/07/2017 28/07/2017 Fort Worth Texas - Etats-Unis Proceedings IEEE Importance : pp 2768 - 2771 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] algorithme Graph-Cut [Termes IGN] analyse comparative [Termes IGN] champ aléatoire conditionnel [Termes IGN] classification par maximum de vraisemblance [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] inférence [Termes IGN] semis de points [Termes IGN] test de performance [Termes IGN] théorie de Dempster-Shafer Résumé : (auteur) In this paper, we focus on the classification of lidar point cloud data acquired via mobile laser scanning, whereby the classification relies on a context model based on a Conditional Random Field (CRF). We present two approximate inference algorithms based on belief propagation, as well as a graph-cut-based approach not yet applied in this context. To demonstrate the performance of our approach, we present the classification results derived for a standard benchmark dataset. These results clearly indicate that the graph-cut-based method is able to retrieve a labeling of higher likelihood in only a fraction of the time needed for the other approaches. The higher likelihood, in turn, translates into a significant gain in the accuracy of the obtained classification. Numéro de notice : C2017-026 Affiliation des auteurs : IGN+Ext (2012-2019) Autre URL associée : vers HAL Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/IGARSS.2017.8127571 Date de publication en ligne : 04/12/2017 En ligne : https://doi.org/10.1109/IGARSS.2017.8127571 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89289

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How to combine lidar and very high resolution multispectral images for forest stand segmentation? / Clément Dechesne (2017)  

Public Titre : How to combine lidar and very high resolution multispectral images for forest stand segmentation? Type de document : Article/Communication Auteurs : Clément Dechesne , Auteur ; Clément Mallet , Auteur ; Arnaud Le Bris , Auteur ; Valérie Gouet-Brunet , Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2017 Projets : 2-Pas d'info accessible - article non ouvert / Conférence : IGARSS 2017, IEEE International Geoscience And Remote Sensing Symposium 23/07/2017 28/07/2017 Fort Worth Texas - Etats-Unis Proceedings IEEE Importance : pp 2772 - 2775 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] image infrarouge [Termes IGN] image multibande [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] peuplement forestier [Termes IGN] segmentation sémantique [Termes IGN] semis de points [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) Forest stands are a basic unit of analysis for forest inventory and mapping. Stands are defined as large forested areas of homogeneous tree species composition and age. Their accurate delineation is usually performed by human operators through visual analysis of very high resolution (VHR) infra-red and visible images. This task is tedious, highly time consuming, and needs to be automated for scalability and efficient updating purposes. The most appropriate fusion of two remote sensing modalities (lidar and multispectral images) is investigated here. The multispectral images give information about the tree species while 3D lidar point clouds provide geometric information. The fusion is operated at three different levels within a semantic segmentation workflow: over-segmentation, classification, and regularization. Results show that over-segmentation can be performed either on lidar or optical images without performance loss or gain, whereas fusion is mandatory for efficient semantic segmentation. Eventually, the fusion strategy dictates the composition and nature of the forest stands, assessing the high versatility of our approach. Numéro de notice : C2017-039 Affiliation des auteurs : LASTIG MATIS (2012-2019) Thématique : FORET/IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/IGARSS.2017.8127572 Date de publication en ligne : 04/12/2017 En ligne : https://doi.org/10.1109/IGARSS.2017.8127572 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91921

New iterative learning strategy to improve classification systems by using outlier detection techniques / Charlotte Pelletier (2017)  

Public Titre : New iterative learning strategy to improve classification systems by using outlier detection techniques Type de document : Article/Communication Auteurs : Charlotte Pelletier, Auteur ; Silvia Valero, Auteur ; Jordi Inglada, Auteur ; Gérard Dedieu, Auteur ; Nicolas Champion , Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2017 Conférence : IGARSS 2017, IEEE International Geoscience And Remote Sensing Symposium 23/07/2017 28/07/2017 Fort Worth Texas - Etats-Unis Proceedings IEEE Importance : pp 3676 - 3679 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] apprentissage automatique [Termes IGN] classification dirigée [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] détection d'anomalie [Termes IGN] itération [Termes IGN] valeur aberrante Résumé : (auteur) The supervised classification of satellite image time series allows obtaining reliable land cover maps over large areas. However, their quality depends on the reference datasets used for training the classifier. In remote sensing, reference data may lack of timeliness and accuracy which leads to the presence of mislabeled data degrading the classification performances. This work presents an iterative learning framework to deal with noisy instances, that can be seen as outliers. Several outlier detection strategies, based on the well-known Random Forests (RF) ensemble classifier, are proposed, evaluated quantitatively, and then compared with traditional methods. Experimental results have been carried out by using synthetic and real datasets representing annual vegetation profiles. Numéro de notice : C2017-042 Affiliation des auteurs : IGN+Ext (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/IGARSS.2017.8127796 Date de publication en ligne : 04/12/2017 En ligne : https://doi.org/10.1109/IGARSS.2017.8127796 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91925