440 lines
24 KiB
BibTeX
440 lines
24 KiB
BibTeX
@article{ColasFat2020,
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title = {Fat {{Pad Cages}} for {{Facial Posing}}},
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author = {Colas, Adèle and Guiotte, Florent and Danieau, Fabien and Clerc,
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François Le and Avril, Quentin},
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date = {2020-10-12},
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number = {arXiv:2010.05528},
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eprint = {2010.05528},
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eprinttype = {arxiv},
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primaryclass = {cs},
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publisher = {{arXiv}},
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doi = {10.48550/arXiv.2010.05528},
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url = {http://arxiv.org/abs/2010.05528},
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urldate = {2023-01-25},
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abstract = {We introduce Fat Pad cages for posing facial meshes. It combines
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cage representation and facial anatomical elements, and enables
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users with no artistic skill to quickly sketch realistic facial
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expressions. The model relies on one or several cage(s) that
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deform(s) the mesh following the human fat pads map. We propose a
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new function to filter Green Coordinates using geodesic distances
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preventing global deformation while ensuring smooth deformations at
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the borders. Lips, nostrils and eyelids are processed slightly
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differently to allow folding up and opening. Cages are
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automatically created and fit any new unknown facial mesh. To
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validate our approach, we present a user study comparing our Fat
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Pad cages to regular Green Coordinates. Results show that Fat Pad
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cages bring a significant improvement in reproducing existing
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facial expressions.},
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archiveprefix = {arXiv},
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keywords = {mine},
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file = {/home/florent/.zotero/data/storage/EKVVUHR9/Colas et al. - 2020 - Fat
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Pad Cages for Facial
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Posing.pdf;/home/florent/.zotero/data/storage/42TANDRQ/2010.html},
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arXiv = {2010.05528},
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pdf = {https://arxiv.org/pdf/2010.05528},
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shortjournal = {arXiv},
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}
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@thesis{Guiotte2d2021,
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title = {{{2D}}/{{3D}} Discretization of {{Lidar}} Point Clouds: {{Processing}
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} with Morphological Hierarchies and Deep Neural Networks},
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shorttitle = {{{2D}}/{{3D}} Discretization of {{Lidar}} Point Clouds},
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author = {Guiotte, Florent},
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date = {2021-01-25},
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institution = {{Université Rennes 2}},
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location = {{Rennes}},
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abstract = {This thesis evaluates the relevance of morphological hierarchies
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and deep neural networks for analysing LiDAR data by means of
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several discretization strategies. The quantity of data increases
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exponentially in coverage and resolution. However, actual datasets
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are not yet fully exploited due to the lack of efficient
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methodological tools for this specific type of data. Morphological
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structures are known to extract reliable multi-scale features while
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being extremely computationally efficient. In the mean time, the
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tremendous breakthrough of deep learning in computer vision has
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shaken up the remote sensing community. To this end we define and
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evaluate different discretization strategies of LiDAR data. In a
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first part, we re-organise the point clouds into 2D regular grids.
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We propose to derive several LiDAR features, trying to extract
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complete elevation description and spectral values along with LiDAR
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specific information. In a second part we re-organise the point
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clouds into 3D regular grids. The regular grids are sufficient to
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provide the neighboring context needed for the morphological
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hierarchies, and the proposed grids are also adapted to the input
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layers of state-of-the-art deep neural networks. The different
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methods are systematically validated in remote sensing scenarios.},
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bibtex_show = {true},
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hal_id = {tel-03385817},
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hal_version = {v1},
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langid = {english},
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pdf = {Guiotte - 2021 - 2D3D discretization of Lidar point clouds Proces.pdf},
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keywords = {mine},
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file = {/home/florent/.zotero/data/storage/3AUGKHQS/Guiotte - 2021 - 2D3D
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discretization of Lidar point clouds
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Proces.pdf;/home/florent/.zotero/data/storage/ZBHU3Q2H/Guiotte - 2021 -
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2D3D discretization of Lidar point clouds Proces.pdf},
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selected = {true},
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}
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@incollection{GuiotteAttribute2019,
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title = {Attribute Filtering of Urban Point Clouds Using Max-Tree on Voxel
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Data},
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booktitle = {Mathematical Morphology and Its Applications to Signal and Image
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Processing},
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author = {Guiotte, Florent and Lefèvre, Sébastien and Corpetti, Thomas},
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date = {2019-05},
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shortjournal = {ISMM},
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pages = {391--402},
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doi = {10.1007/978-3-030-20867-7\_30},
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url = {https://hal.archives-ouvertes.fr/hal-02343890},
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abstract = {This paper deals with morphological characterization of
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un-structured 3D point clouds issued from LiDAR data. A large
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majority of studies first rasterize 3D point clouds onto regular 2D
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grids and then use standard 2D image processing tools for
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characterizing data. In this paper, we suggest instead to keep the
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3D structure as long as possible in the process. To this end, as
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raw LiDAR point clouds are unstructured, we first propose some
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voxelization strategies and then extract some morphological
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features on voxel data. The results obtained with attribute
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filtering show the ability of this process to efficiently extract
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useful information .},
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hal_id = {hal-02343890},
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hal_version = {v1},
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pdf = {https://hal.archives-ouvertes.fr/hal-02343890/file/ismm2019.pdf},
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keywords = {mine},
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annotation = {00000},
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file = {/home/florent/.zotero/data/storage/M92S96E2/Guiotte et al. - 2019 -
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Attribute filtering of urban point clouds using ma.pdf},
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}
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@article{GuiotteFiltrage2019,
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title = {Filtrage et classification de nuage de points sur la base d'attributs
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morphologiques},
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author = {Guiotte, Florent and Lefèvre, Sébastien and Corpetti, Thomas},
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date = {2019},
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journaltitle = {Journées francophones des jeunes chercheurs en vision par
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ordinateur},
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shortjournal = {ORASIS},
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pages = {9},
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abstract = {Cet article traite de l’analyse de données LiDAR via la
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caractérisation morphologique des nuages de points qui en
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résultent. Tandis que la majorité de travaux effectuent en premier
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lieu une «rasterisation» (transformation du nuage de point en
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données 2D structurées en pixels) et utilisent ensuite des outils
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d’analyse d’images, nous proposons ici de garder le plus longtemps
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possible la structure 3D (en y calculant des caractéristiques) et
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de structurer les données le plus tard possible. En pratique, une
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étape de voxelisation des données brutes est opérée afin d’utiliser
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des outils mathématiques définis sur des volumes réguliers. Ensuite
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, nous utilisons des représentations hiérarchiques pour
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caractériser ces voxels. Pour illustrer les intérêts d’une telle
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approche, plusieurs applications sont proposées, notamment le
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débruitage, le filtrage et la classification des nuages de points},
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hal_id = {hal-02343933},
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langid = {french},
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keywords = {mine},
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annotation = {00000},
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file = {/home/florent/.zotero/data/storage/YVS8PZP6/Guiotte et al. - 2019 -
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Filtrage et classification de nuage de points sur .pdf},
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}
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@article{GuiotteInteractive2020,
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title = {Interactive {{Digital Terrain Model Analysis}} in {{Attribute Space}}
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},
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author = {Guiotte, Florent and Etaix, Geoffroy and Lefèvre, Sébastien and
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Corpetti, Thomas},
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date = {2020},
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journaltitle = {International Archives of the Photogrammetry, Remote Sensing
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and Spatial Information Sciences},
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shortjournal = {ISPRS},
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volume = {XLIII-B2-2020},
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pages = {1203--1209},
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doi = {10.5194/isprs-archives-XLIII-B2-2020-1203-2020},
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url = {
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https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B2-2020/1203/2020/
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},
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abstract = {The use of high-resolution digital terrain model derived from
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airborne LiDAR system becomes more and more prevalent. Effective
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multi-scale structure characterization is of crucial importance for
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various domains such as geosciences, archaeology and Earth
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observation. This paper deals with structure detection in large
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datasets with little or no prior knowledge. In a recent work, we
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have demonstrated the relevance of hierarchical representations to
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enhance the description of digital elevation models (Guiotte et al.
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, 2019). In this paper, we proceed further and use the pattern
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spectrum, a multi-scale tool originating from mathematical
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morphology, further enhanced by hierarchical representations. The
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pattern spectra allow to globally and efficiently compute the
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distribution of size and shapes of the objects contained in a
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digital elevation model. The tree-based pattern spectra used in
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this paper allowed us to analyse and extract features of interest.
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We report experiments in a natural environment with two use cases,
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related to gold panning and dikes respectively. The process is fast
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enough to allow interactive analysis.},
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hal_id = {hal-03065475},
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keywords = {dtm,mine},
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annotation = {0 citations (Crossref) [2021-06-10] 00000},
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file = {/home/florent/.zotero/data/storage/CX6TK8BC/Guiotte et al. - 2020 -
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Interactive Digital Terrain Model Analysis in Attr.pdf},
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}
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@inproceedings{GuiotteRasterization2019,
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title = {Rasterization Strategies for Airborne {{LiDAR}} Classification Using
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Attribute Profiles},
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booktitle = {2019 {{Joint Urban Remote Sensing Event}}},
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author = {Guiotte, Florent and Lefèvre, Sébastien and Corpetti, Thomas},
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date = {2019},
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shortjournal = {JURSE},
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pages = {1--4},
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publisher = {{IEEE}},
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doi = {10.1109/JURSE.2019.8808945},
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url = {https://hal.archives-ouvertes.fr/hal-02343901/document},
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abstract = {This paper evaluates rasterization strategies and the benefit of
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hierarchical representations, in particular attribute profiles, to
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classify urban scenes issued from multispectral LiDAR acquisitions.
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In recent years it has been found that rasterized LiDAR provides a
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reliable source of information on its own or for fusion with
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multispectral/hyperspectral imagery. However previous works using
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attribute profiles on LiDAR rely on elevation data only. Our
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approach focuses on several LiDAR features rasterized with
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multilevel description to produce precise land cover maps over
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urban areas. Our experimental results obtained with LiDAR data from
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university of Houston indicate good classification results for
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alternative rasters and even more when multilevel image
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descriptions are used.},
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hal_id = {hal-02343901v2},
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keywords = {ISMM,mine},
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annotation = {2 citations (Crossref) [2021-06-10] 00000},
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file = {/home/florent/.zotero/data/storage/RCPIJU9B/Guiotte et al. -
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Rasterization strategies for airborne LiDAR classi.pdf},
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}
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@article{GuiotteRelation2020,
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title = {Relation {{Network}} for {{Full-waveforms LiDAR Classification}}},
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author = {Guiotte, Florent and Rao, Mengbin and Lefèvre, Sébastien and Tang,
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Ping and Corpetti, Thomas},
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date = {2020},
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journaltitle = {International Archives of the Photogrammetry, Remote Sensing
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and Spatial Information Sciences},
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shortjournal = {ISPRS},
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volume = {XLIII-B3-2020},
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pages = {515--520},
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doi = {10.5194/isprs-archives-XLIII-B3-2020-515-2020},
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url = {
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https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B3-2020/515/2020/
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},
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abstract = {LiDAR data are widely used in various domains related to
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geosciences (flow, erosion, rock deformations, etc.), computer
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graphics (3D reconstruction) or earth observation (detection of
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trees, roads, buildings, etc.). Because of the unstructured nature
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of remaining 3D points and because of the cost of acquisition, the
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LiDAR data processing is still challenging (few learning data,
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difficult spatial neighboring relationships, etc.). In practice,
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one can directly analyze the 3D points using feature extraction and
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then classify the points via machine learning techniques (Brodu,
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Lague, 2012, Niemeyer et al., 2014, Mallet et al., 2011). In
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addition, recent neural network developments have allowed precise
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point cloud segmentation, especially using the seminal pointnet
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network and its extensions (Qi et al., 2017a, Riegler et al.,
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2017). Other authors rather prefer to rasterize / voxelize the
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point cloud and use more conventional computers vision strategies
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to analyze structures (Lodha et al., 2006). In a recent work, we
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demonstrated that Digital Elevation Models (DEM) is reductive of
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the vertical component complexity describing objects in urban
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environments (Guiotte et al., 2020). These results highlighted the
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necessity to preserve the 3D structure of the point cloud as long
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as possible in the processing. In this paper, we therefore rely on
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ortho-waveforms to compute a land cover map. Ortho-waveforms are
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directly computed from the waveforms in a regular 3D grid. This
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method provides volumes somehow "similar" to hyperspectral data
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where each pixel is here associated with one ortho-waveform. Then,
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we exploit efficient neural networks adapted to the classification
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of hyperspectral data when few samples are available. Our results,
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obtained on the 2018 Data Fusion Contest dataset (DFC), demonstrate
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the efficiency of the approach.},
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hal_id = {hal-03045729},
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keywords = {mine},
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annotation = {0 citations (Crossref) [2021-06-10] 00000},
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file = {/home/florent/.zotero/data/storage/VBX3GYM4/Guiotte et al. - 2020 -
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RELATION NETWORK FOR FULL-WAVEFORMS LIDAR CLASSIFI.pdf},
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}
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@article{GuiotteSemantic2020,
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title = {Semantic Segmentation of {{LiDAR}} Points Clouds: {{Rasterisation}}
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beyond Digital Elevation Models},
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author = {Guiotte, Florent and Pham, Minh-Tan and Dambreville, Romain and
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Corpetti, Thomas and Lefèvre, Sébastien},
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date = {2020-01},
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journaltitle = {IEEE Geoscience and Remote Sensing Letters},
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shortjournal = {GRSL},
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publisher = {{IEEE - Institute of Electrical and Electronics Engineers}},
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doi = {10.1109/LGRS.2019.2958858},
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url = {https://hal.archives-ouvertes.fr/hal-02399410},
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abstract = {LiDAR point clouds are receiving a growing interest in remote
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sensing as they provide rich information to be used independently
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or together with optical data sources such as aerial imagery.
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However, their non-structured and sparse nature make them difficult
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to handle, conversely to raw imagery for which many efficient tools
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are available. To overcome this specific nature of LiDAR point
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clouds, standard approaches often rely in converting the point
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cloud into a digital elevation model, represented as a 2D raster.
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Such a raster can then be used similarly as optical images, e.g.
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with 2D convolutional neural networks for semantic segmentation. In
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this letter, we show that LiDAR point clouds provide more
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information than only the DEM, and that considering alternative
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rasterization strategies helps to achieve better semantic
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segmentation results. We illustrate our findings on the IEEE DFC
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2018 dataset.},
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hal_id = {hal-02399410},
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hal_version = {v1},
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pdf = {https://hal.archives-ouvertes.fr/hal-02399410/file/grsl.pdf},
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keywords = {deep learning,lidar,mine},
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annotation = {0 citations (Crossref) [2021-06-10] 00000},
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file = {/home/florent/.zotero/data/storage/FEUUXDVN/Guiotte et al. - 2020 -
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Semantic segmentation of łd points clouds Rasteri.pdf},
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}
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@article{GuiotteStrategies2019,
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title = {Stratégies de rastérisation pour la classification de données LiDAR
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aéroportées par profils d'attributs morphologiques},
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author = {Guiotte, Florent and Lefèvre, Sébastien and Corpetti, Thomas},
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date = {2019},
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journaltitle = {Colloque GRETSI sur le Traitement du Signal et des Images},
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shortjournal = {GRETSI},
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pages = {5},
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abstract = {Cet article traite de rastérisation par représentations
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hiérarchiques (en particulier via les profils d'attributs
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mor-phologiques) de nuages de points 3D. Lorsque ces données
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proviennent d'appareils LiDAR, il est fréquent de les rastériser
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pour fournir une carte d'élévation (exploitée seule ou combinée
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avec des images multi-ou hyperspectrales). Bien que certains
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travaux utilisent des profils d'attributs sur de telles données
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d'élévation, nous mettons ici l'accent sur plusieurs
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caractéristiques LiDAR rastérisées (liées aux échos, retours d'onde
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, etc.) et sur une description multi-échelle pour produire des
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cartes d'occupation du sol précises sur des zones urbaines. Nos
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résultats expérimentaux obtenus avec les données LiDAR de
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l'université de Houston indiquent de bons résultats de
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classification en exploitant nos rasters.},
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bibtex_show = {true},
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hal_id = {hal-02343958},
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||
langid = {french},
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||
keywords = {lidar,mine},
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||
annotation = {00000},
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||
file = {/home/florent/.zotero/data/storage/BMGP8AJA/Guiotte et al. - 2019 -
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Stratégies de rastérisation pour la classification.pdf},
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}
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@inproceedings{GuiotteVoxelbased2019,
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title = {Voxel-Based Attribute Profiles on Lidar Data for Land Cover Mapping},
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booktitle = {{{IEEE}} International Geosciences and Remote Sensing Symposium},
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author = {Guiotte, Florent and Lefèvre, Sébastien and Corpetti, Thomas},
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date = {2019},
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shortjournal = {IGARSS},
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location = {{Yokohama, Japan}},
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doi = {10.1109/IGARSS.2019.8899129},
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url = {https://hal.archives-ouvertes.fr/hal-02343963},
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||
abstract = {This paper deals with strategies for LiDAR data analysis. While a
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large majority of studies first rasterize 3D point clouds onto
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regular 2D grids and then use 2D image processing tools for
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characterizing data, our work rather suggests to keep as long as
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possible the 3D structure by computing features on 3D data and
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rasterize later in the process. By this way, the vertical component
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is still taken into account. In practice, a voxelization step of
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raw data is performed in order to exploit mathematical tools
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defined on regular volumes. More precisely, we focus on attribute
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profiles that have been shown to be very efficient features to
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characterize remote sensing scenes. They require the computation of
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an underlying hierarchical structure (through a Max-Tree).
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Experimental results obtained on urban LiDAR data classification
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support the performances of this strategy compared with an early
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rasterization process.},
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hal_id = {hal-02343963},
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||
hal_version = {v1},
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||
keywords = {attribute profiles,land cover mapping,max-tree,mine,multiscale
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||
representation,voxelization},
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||
annotation = {0 citations (Crossref) [2021-06-10] 00000},
|
||
file = {/home/florent/.zotero/data/storage/65K47AUD/Guiotte et al. - 2019 -
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Voxel-based attribute profiles on LiDAR data for l.pdf},
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}
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@article{LeLearning2022,
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title = {Learning {{Digital Terrain Models From Point Clouds}}: {{ALS2DTM
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Dataset}} and {{Rasterization-Based GAN}}},
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shorttitle = {Learning {{Digital Terrain Models From Point Clouds}}},
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author = {Lê, Hoàng-Ân and Guiotte, Florent and Pham, Minh-Tan and Lefèvre,
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Sébastien and Corpetti, Thomas},
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date = {2022},
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journaltitle = {IEEE Journal of Selected Topics in Applied Earth Observations
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and Remote Sensing},
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shortjournal = {JSTARS},
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volume = {15},
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eprint = {2206.03778},
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eprinttype = {arxiv},
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pages = {4980--4989},
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issn = {2151-1535},
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doi = {10.1109/JSTARS.2022.3182030},
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abstract = {Despite the popularity of deep neural networks in various domains,
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the extraction of digital terrain models (DTMs) from airborne laser
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scanning (ALS) point clouds is still challenging. This might be due
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to the lack of the dedicated large-scale annotated dataset and the
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data-structure discrepancy between point clouds and DTMs. To
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promote data-driven DTM extraction, this article collects from open
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sources a large-scale dataset of ALS point clouds and corresponding
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DTMs with various urban, forested, and mountainous scenes. A
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baseline method is proposed as the first attempt to train a deep
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neural network to extract DTMs directly from ALS point clouds via
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rasterization techniques, coined DeepTerRa. Extensive studies with
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well-established methods are performed to benchmark the dataset and
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analyze the challenges in learning to extract DTM from point
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clouds. The experimental results show the interest of the agnostic
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data-driven approach, with submetric error level compared to
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methods designed for DTM extraction. The data and source code are
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available online at https://lhoangan.github.io/deepterra/ for
|
||
reproducibility and further similar research.},
|
||
archiveprefix = {arXiv},
|
||
eventtitle = {{{IEEE Journal}} of {{Selected Topics}} in {{Applied Earth
|
||
Observations}} and {{Remote Sensing}}},
|
||
hal_id = {hal-03717178},
|
||
pdf = {https://arxiv.org/pdf/2206.03778.pdf},
|
||
keywords = {deep learning,gan,lidar,mine,rasterization},
|
||
annotation = {00001},
|
||
file = {/home/florent/.zotero/data/storage/V7CAY453/Lê et al. - 2022 -
|
||
Learning Digital Terrain Models From Point
|
||
Clouds.pdf;/home/florent/.zotero/data/storage/BDA5G3YP/9794452.html},
|
||
}
|
||
|
||
@article{MaiaClassification2021,
|
||
title = {Classification of {{Remote Sensing Data With Morphological Attribute
|
||
Profiles}}: {{A}} Decade of Advances},
|
||
shorttitle = {Classification of {{Remote Sensing Data With Morphological
|
||
Attribute Profiles}}},
|
||
author = {Maia, Deise Santana and Pham, Minh-Tan and Aptoula, Erchan and
|
||
Guiotte, Florent and Lefèvre, Sébastien},
|
||
date = {2021-09},
|
||
journaltitle = {IEEE Geoscience and Remote Sensing Magazine},
|
||
shortjournal = {GRSM},
|
||
volume = {9},
|
||
number = {3},
|
||
pages = {43--71},
|
||
issn = {2168-6831},
|
||
doi = {10.1109/MGRS.2021.3051859},
|
||
abstract = {Morphological attribute profiles (APs) are among the most
|
||
prominent methods for spatial–spectral pixel analysis of remote
|
||
sensing images. Since their introduction a decade ago to tackle
|
||
land cover classification, many studies have been contributed to
|
||
the state of the art, focusing not only on their application to a
|
||
wider range of tasks but also on their performance improvement and
|
||
extension to more complex Earth observation data.},
|
||
eventtitle = {{{IEEE Geoscience}} and {{Remote Sensing Magazine}}},
|
||
hal_id = {hal-03199357},
|
||
pdf = {https://hal.science/hal-03199357v1/document},
|
||
keywords = {attribute profiles,mine},
|
||
annotation = {00010},
|
||
file = {/home/florent/.zotero/data/storage/6WDRKG9L/Maia et al. - 2021 -
|
||
Classification of Remote Sensing Data With
|
||
Morphol.pdf;/home/florent/.zotero/data/storage/ZHLN4422/Maia et al. -
|
||
2021 - Classification of Remote Sensing Data With
|
||
Morphol.pdf;/home/florent/.zotero/data/storage/YASAD9GI/9366293.html},
|
||
}
|