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Adaptive edge preserving maps in Markov random fields for hyperspectral image classification / Chao Pan in IEEE Transactions on geoscience and remote sensing, vol 59 n° 10 (October 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 10 (October 2021) . - pp 8568 - 8583 Titre : Adaptive edge preserving maps in Markov random fields for hyperspectral image classification Type de document : Article/Communication Auteurs : Chao Pan, Auteur ; Xiuping Jia, Auteur ; Jie Li, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 8568 - 8583 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] accentuation de contours [Termes IGN] algorithme Graph-Cut [Termes IGN] champ aléatoire de Markov [Termes IGN] classe d'objets [Termes IGN] détection de contours [Termes IGN] étiquette de classe [Termes IGN] image AVIRIS [Termes IGN] image hyperspectrale [Termes IGN] optimisation (mathématiques) [Termes IGN] segmentation d'image Résumé : (auteur) This article presents a novel adaptive edge preserving (aEP) scheme in Markov random fields (MRFs) for hyperspectral image (HSI) classification. MRF regularization usually suffered from over-smoothing at boundaries and insufficient refinement within class objects. This work divides and conquers this problem class-by-class, and integrates K ( K−1 )/2 ( K is the class number) aEP maps (aEPMs) in MRF model. Spatial label dependence measure (SLDM) is designed to estimate the interpixel label dependence for given spectral similarity measure. For each class pair, aEPM is optimized by maximizing the difference between intraclass and interclass SLDM. Then, aEPMs are integrated with multilevel logistic (MLL) model to regularize the raw pixelwise labeling obtained by spectral and spectral–spatial methods, respectively. The graph-cuts-based α β -swap algorithm is modified to optimize the designed energy function. Moreover, to evaluate the final refined results at edges and small details thoroughly, segmentation evaluation metrics are introduced. Experiments conducted on real HSI data denote the superiority of aEPMs in evaluation metrics and region consistency, especially in detail preservation. Numéro de notice : A2021-713 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3035642 Date de publication en ligne : 16/11/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3035642 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98618 [article]

Scalable surface reconstruction with Delaunay-Graph neural networks / Raphaël Sulzer in Computer graphics forum, vol 40 n° 5 (2021)  

Public [article]  inComputer graphics forum > vol 40 n° 5 (2021) . - pp 157 - 167 Titre : Scalable surface reconstruction with Delaunay-Graph neural networks Type de document : Article/Communication Auteurs : Raphaël Sulzer , Auteur ; Loïc Landrieu , Auteur ; Renaud Marlet, Auteur ; Bruno Vallet , Auteur Année de publication : 2021 Projets : BIOM / Vallet, Bruno Conférence : SGP 2021, Symposium on Geometry Processing 12/07/2021 14/07/2021 Toronto Ontario - Canada open access proceedings Article en page(s) : pp 157 - 167 Note générale : bibliographie The presentation of this work at SGP 2021 is available at https://youtu.be/KIrCDGhS10o Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] algorithme Graph-Cut [Termes IGN] apprentissage profond [Termes IGN] prise en compte du contexte [Termes IGN] reconstruction d'objet [Termes IGN] réseau neuronal de graphes [Termes IGN] semis de points [Termes IGN] tétraèdre [Termes IGN] triangulation de Delaunay Résumé : (auteur) We introduce a novel learning-based, visibility-aware, surface reconstruction method for large-scale, defect-laden point clouds. Our approach can cope with the scale and variety of point cloud defects encountered in real-life Multi-View Stereo (MVS) acquisitions. Our method relies on a 3D Delaunay tetrahedralization whose cells are classified as inside or outside the surface by a graph neural network and an energy model solvable with a graph cut. Our model, making use of both local geometric attributes and line-of-sight visibility information, is able to learn a visibility model from a small amount of synthetic training data and generalizes to real-life acquisitions. Combining the efficiency of deep learning methods and the scalability of energy-based models, our approach outperforms both learning and non learning-based reconstruction algorithms on two publicly available reconstruction benchmarks. Numéro de notice : A2021-400 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/cgf14364 En ligne : https://doi.org/10.1111/cgf.14364 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98219 [article]

Efficiently distributed watertight surface reconstruction / Laurent Caraffa (2021)  

Public Titre : Efficiently distributed watertight surface reconstruction Type de document : Article/Communication Auteurs : Laurent Caraffa , Auteur ; Yanis Marchand , Auteur ; Mathieu Brédif , Auteur ; Bruno Vallet , Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2021 Projets : 1-Pas de projet / Vallet, Bruno Conférence : 3DV 2021, International Conference on 3D Vision 01/12/2021 03/12/2021 Londres online Royaume-Uni Proceedings IEEE Importance : pp 1432 - 1441 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] algorithme Graph-Cut [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] reconstruction d'objet [Termes IGN] scène [Termes IGN] semis de points [Termes IGN] Spark [Termes IGN] triangulation de Delaunay Résumé : (auteur) We present an out-of-core and distributed surface reconstruction algorithm which scales efficiently on arbitrarily large point clouds (with optical centres) and produces a 3D watertight triangle mesh representing the surface of the underlying scene. Surface reconstruction from a point cloud is a difficult problem and existing state of the art approaches are usually based on complex pipelines making use of global algorithms (i.e. Delaunay triangulation, graph-cut optimisation). For one of these approaches, we investigate the distribution of all the steps (in particular Delaunay triangulation and graph-cut optimisation) in order to propose a fully scalable method. We show that the problem can be tiled and distributed across a cloud or a cluster of PCs by paying a careful attention to the interactions between tiles and using Spark computing framework. We confirm the efficiency of this approach with an in-depth quantitative evaluation and the successful reconstruction of a surface from a very large data set which combines more than 350 million aerial and terrestrial LiDAR points. Numéro de notice : C2021-037 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/3DV53792.2021.00150 En ligne : https://doi.org/10.1109/3DV53792.2021.00150 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99167

Seamline network generation based on foreground segmentation for orthoimage mosaicking / Li Li in ISPRS Journal of photogrammetry and remote sensing, vol 148 (February 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 148 (February 2019) . - pp 41 - 53 Titre : Seamline network generation based on foreground segmentation for orthoimage mosaicking Type de document : Article/Communication Auteurs : Li Li, Auteur ; Jingming Tu, Auteur ; Ye Gong, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 41 - 53 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] algorithme Graph-Cut [Termes IGN] mosaïquage d'images [Termes IGN] optimisation (mathématiques) [Termes IGN] orthoimage [Termes IGN] orthophotoplan numérique [Termes IGN] raccord d'images Résumé : (Auteur) For multiple orthoimages mosaicking, the detection of an optimal seamline in an overlapped region and the generation of a seamline network are two key issues for creating a seamless and pleasant large-scale digital orthophoto map. In this paper, a novel system is proposed to generate the large-scale orthophoto by mosaicking multiple orthoimages via Graph cuts. The proposed system is comprised of two parts. In the first part, to ensure that the detected seamline avoids crossing the obvious objects, a novel foreground segmentation-based approach is proposed to detect the optimal seamline for two adjacent images. The foreground objects are segmented from the overlapped region at the superpixel level followed by the pixel-level seamline optimization. In the second part, we propose a novel seamline network generation approach to produce the large-scale orthophoto by mosaicking multiple orthoimages. The pairwise and junction regions extracted from the initial network are refined using two-label and multi-label Graph cuts, respectively. The key advantage of our proposed seamline network is that junction points can be automatically and optimally found using the multi-label Graph cuts. The experimental results on two groups of orthoimages show that our proposed system can generate high-quality seamline networks with less artifacts, and that it outperforms the state-of-the-art algorithm and the commercial software based on visual comparison and statistical evaluation. Numéro de notice : A2019-071 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.12.002 Date de publication en ligne : 20/12/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.12.002 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92158 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2019021	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2019023	DEP-RECP	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2019022	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Seamline optimisation for urban aerial ortho‐image mosaicking using graph cuts / Yunsheng Zhang in Photogrammetric record, vol 33 n° 161 (March 2018)  

Public [article]  inPhotogrammetric record > vol 33 n° 161 (March 2018) . - pp 131 - 147 Titre : Seamline optimisation for urban aerial ortho‐image mosaicking using graph cuts Type de document : Article/Communication Auteurs : Yunsheng Zhang, Auteur ; Minglei Zhang, Auteur ; Shouji Du, Auteur ; Zhengrong Zou, Auteur ; Chong Fan, Auteur Année de publication : 2018 Article en page(s) : pp 131 - 147 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Orthophotographie, orthoimage [Termes IGN] algorithme Graph-Cut [Termes IGN] optimisation (mathématiques) [Termes IGN] orthophotoplan numérique [Termes IGN] raccord d'images [Termes IGN] zone urbaine Résumé : (Auteur) Optimal seamline detection is a key step when composing digital orthophotomaps (DOM) of extensive areas from overlapping ortho‐images. To avoid seamlines passing through buildings when mosaicking, seamline detection between adjacent ortho‐images is casted as a graph‐cut problem. To avoid buildings, colour differences and gradient magnitude derived from the ortho‐images, together with approximate object heights derived from a digital surface model (DSM), are combined for constructing the cost term of the graph. To efficiently obtain an approximate height value for each DOM unit, an expanded normalised DSM is proposed. Furthermore, the designed cost term for final composite ortho‐image merging is weighted to prioritise pixels closest to the nadir point. Experiments with strips of aerial images show that the proposed method obtains seamless mosaicking of ortho‐images without manual work. Numéro de notice : A2018-222 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/phor.12232 Date de publication en ligne : 25/03/2018 En ligne : https://doi.org/10.1111/phor.12232 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90042 [article]

Bayesian graph-cut optimization for wall surfaces reconstruction in indoor environments / Georgios-Tsampikos Michailidis in The Visual Computer, vol 33 n° 10 (October 2017)  

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Deep supervised and contractive neural network for SAR image classification / Jie Geng in IEEE Transactions on geoscience and remote sensing, vol 55 n° 4 (April 2017)  

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

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3D watertight mesh generation with uncertainties from ubiquitous data / Laurent Caraffa (2016)    

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Forest stand segmentation using airborne lidar data and very high resolution multispectral imagery / Clément Dechesne (2016)    

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Extracting mobile objects in images using a Velodyne lidar point cloud / Bruno Vallet in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol II-3 W4 (March 2015)  

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3D octree based watertight mesh generation from ubiquitous data / Laurent Caraffa (2015)  

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A graph-based classification method for hyperspectral images / J. Bai in IEEE Transactions on geoscience and remote sensing, vol 51 n° 2 (February 2013)  

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