185 lines
13 KiB
BibTeX
185 lines
13 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, 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 cage representation and facial anatomical elements, and enables users with no artistic skill to quickly sketch realistic facial expressions. The model relies on one or several cage(s) that deform(s) the mesh following the human fat pads map. We propose a new function to filter Green Coordinates using geodesic distances preventing global deformation while ensuring smooth deformations at the borders. Lips, nostrils and eyelids are processed slightly differently to allow folding up and opening. Cages are automatically created and fit any new unknown facial mesh. To validate our approach, we present a user study comparing our Fat Pad cages to regular Green Coordinates. Results show that Fat Pad cages bring a significant improvement in reproducing existing 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 Pad Cages for Facial 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}} 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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langid = {english},
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keywords = {mine},
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file = {/home/florent/.zotero/data/storage/ZBHU3Q2H/Guiotte - 2021 - 2D3D discretization of Lidar point clouds Proces.pdf}
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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 Data},
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booktitle = {Mathematical Morphology and Its Applications to Signal and Image 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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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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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 - 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 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 ordinateur (ORASIS)},
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pages = {9},
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abstract = {This paper deals with morphological characterization of unstructured 3D point clouds issued from LiDAR data. A large majority of studies first rasterize 3D point clouds onto regular 2D grids and then use standard 2D image processing tools for characterizing data. In this paper, we suggest instead to keep the 3D structure as long as possible in the process. To this end, as raw LiDAR point clouds are unstructured, we first propose some voxelization strategies and then extract some morphological features on voxel data. The results obtained with attribute filtering show the ability of this process to efficiently extract useful information.},
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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 - 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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author = {Guiotte, F. and Etaix, G. and Lefèvre, S. and Corpetti, T.},
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date = {2020},
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journaltitle = {International Archives of the Photogrammetry, Remote Sensing 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 = {https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B2-2020/1203/2020/},
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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 - 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 Attribute Profiles},
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booktitle = {2019 {{Joint Urban Remote Sensing Event}} ({{JURSE}})},
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author = {Guiotte, Florent and Lefèvre, Sébastien and Corpetti, Thomas},
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date = {2019},
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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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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. - 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, F. and Rao, M. B. and Lefèvre, S. and Tang, P. and Corpetti, T.},
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date = {2020},
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journaltitle = {ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
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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 = {https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B3-2020/515/2020/},
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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 - 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}} beyond Digital Elevation Models},
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author = {Guiotte, Florent and Pham, Minh-Tan and Dambreville, Romain and 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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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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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 - 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 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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pages = {5},
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abstract = {This paper evaluates rasterization strategies and the benefit of hierarchical representations (in particular attribute profiles) to classify point clouds. When such data comes from LiDAR acquisitions, a rasterization process if often performed to build an elevation map (possibly used together with multi or hyperspectral images). While some works use attribute profiles on such elevation data, we rather focus here on several LiDAR features rasterized and on their multilevel description to produce accurate land cover maps over urban areas. Our experimental results obtained on LiDAR data from the university of Houston indicate good classification results based on our rasters.},
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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 - 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 ({{IGARSS}})},
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author = {Guiotte, Florent and Lefèvre, Sébastien and Corpetti, Thomas},
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date = {2019},
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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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hal_id = {hal-02343963},
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hal_version = {v1},
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pdf = {https://hal.archives-ouvertes.fr/hal-02343963/file/igarss2019florent.pdf},
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keywords = {attribute profiles,land cover mapping,max-tree,mine,multiscale representation,voxelization},
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annotation = {0 citations (Crossref) [2021-06-10] 00000},
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file = {/home/florent/.zotero/data/storage/65K47AUD/Guiotte et al. - 2019 - 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 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, Sébastien and Corpetti, Thomas},
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date = {2022},
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journaltitle = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
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volume = {15},
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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, the extraction of digital terrain models (DTMs) from airborne laser scanning (ALS) point clouds is still challenging. This might be due to the lack of the dedicated large-scale annotated dataset and the data-structure discrepancy between point clouds and DTMs. To promote data-driven DTM extraction, this article collects from open sources a large-scale dataset of ALS point clouds and corresponding DTMs with various urban, forested, and mountainous scenes. A baseline method is proposed as the first attempt to train a deep neural network to extract DTMs directly from ALS point clouds via rasterization techniques, coined DeepTerRa. Extensive studies with well-established methods are performed to benchmark the dataset and analyze the challenges in learning to extract DTM from point clouds. The experimental results show the interest of the agnostic data-driven approach, with submetric error level compared to methods designed for DTM extraction. The data and source code are available online at https://lhoangan.github.io/deepterra/ for reproducibility and further similar research.},
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eventtitle = {{{IEEE Journal}} of {{Selected Topics}} in {{Applied Earth Observations}} and {{Remote Sensing}}},
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keywords = {deep learning,gan,lidar,mine,rasterization},
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annotation = {00001},
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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}
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}
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@article{MaiaClassification2021,
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title = {Classification of {{Remote Sensing Data With Morphological Attribute Profiles}}: {{A}} Decade of Advances},
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shorttitle = {Classification of {{Remote Sensing Data With Morphological Attribute Profiles}}},
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author = {Maia, Deise Santana and Pham, Minh-Tan and Aptoula, Erchan and Guiotte, Florent and Lefèvre, Sébastien},
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date = {2021-09},
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journaltitle = {IEEE Geoscience and Remote Sensing Magazine},
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volume = {9},
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number = {3},
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pages = {43--71},
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issn = {2168-6831},
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doi = {10.1109/MGRS.2021.3051859},
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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.},
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eventtitle = {{{IEEE Geoscience}} and {{Remote Sensing Magazine}}},
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keywords = {attribute profiles,mine},
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annotation = {00010},
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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}
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}
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