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A scalable method to construct compact road networks from GPS trajectories / Yuejun Guo in International journal of geographical information science IJGIS, vol 35 n° 7 (July 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 7 (July 2021) . - pp 1309 - 1345 Titre : A scalable method to construct compact road networks from GPS trajectories Type de document : Article/Communication Auteurs : Yuejun Guo, Auteur ; Anton Bardera, Auteur ; Marta Fort, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1309 - 1345 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] chevauchement [Termes IGN] compensation par faisceaux [Termes IGN] contour [Termes IGN] généralisation automatique de données [Termes IGN] méthode heuristique [Termes IGN] noeud [Termes IGN] réseau routier [Termes IGN] segmentation par décomposition-fusion [Termes IGN] squelettisation [Termes IGN] trajectographie par GPS [Termes IGN] trajectoire Résumé : (auteur) The automatic generation of road networks from GPS tracks is a challenging problem that has been receiving considerable attention in the last years. Although dozens of methods have been proposed, current techniques suffer from two main shortcomings: the quality of the produced road networks is still far from those produced manually, and the methods are slow, making them not scalable to large inputs. In this paper, we present a fast four-step density-based approach to construct a road network from a set of trajectories. A key aspect of our method is the use of an improved version of the Slide method to adjust trajectories to build a more compact density surface. The network has comparable or better quality than that of state-of-the-art methods and is simpler (includes fewer nodes and edges). Furthermore, we also propose a split-and-merge strategy that allows splitting the data domain into smaller regions that can be processed independently, making the method scalable to large inputs. The performance of our method is evaluated with extensive experiments on urban and hiking data. Numéro de notice : A2021-447 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1832229 date de publication en ligne : 16/10/2020 En ligne : https://doi.org/10.1080/13658816.2020.1832229 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97859 [article]

Vectorized indoor surface reconstruction from 3D point cloud with multistep 2D optimization / Jiali Han in ISPRS Journal of photogrammetry and remote sensing, vol 177 (July 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 177 (July 2021) . - pp 57 - 74 Titre : Vectorized indoor surface reconstruction from 3D point cloud with multistep 2D optimization Type de document : Article/Communication Auteurs : Jiali Han, Auteur ; Mengqi Rong, Auteur ; Hanqing Jiang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 57 - 74 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] champ aléatoire de Markov [Termes IGN] données lidar [Termes IGN] espace intérieur [Termes IGN] maillage [Termes IGN] programmation linéaire [Termes IGN] Ransac (algorithme) [Termes IGN] reconstruction 3D [Termes IGN] reconstruction d'objet [Termes IGN] segmentation sémantique [Termes IGN] semis de points [Termes IGN] vectorisation Résumé : (Auteur) Vectorized reconstruction from indoor point cloud has attracted increasing attention in recent years due to its high regularity and low memory consumption. Compared with aerial mapping of outdoor urban environments, indoor point cloud generated by LiDAR scanning or image-based 3D reconstruction usually contain more clutter and missing areas, which greatly increase the difficulty of vectorized reconstruction. In this paper, we propose an effective multistep pipeline to reconstruct vectorized models from indoor point cloud without the Manhattan or Atlanta world assumptions. The core idea behind our method is the combination of a sequence of 2D segment or cell assembly problems that are defined as global optimizations while reducing the reconstruction complexity and enhancing the robustness to different scenes. The proposed method includes a semantic segmentation stage and a reconstruction stage. First, we segment the permanent structures of indoor scenes, including ceilings, floors, walls and cylinders, from the input data, and then, we reconstruct these structures in sequence. The floorplan is first generated by detecting wall planes and selecting optimal subsets of projected wall segments with Integer Linear Programming (ILP), followed by constructing a 2D arrangement and recovering the ceiling and floor structures by Markov Random Field (MRF) labeling on the arrangement. Finally, the wall structures are modeled by lifting each edge of the arrangement to a proper height by means of another global optimization. Merging the respective results yields the final model. The experimental results show that the proposed method could obtain accurate and compact vectorized models on both precise LiDAR data and defect-laden MVS data compared with other state-of-the-art approaches. Numéro de notice : A2021-371 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.04.019 date de publication en ligne : 15/05/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.04.019 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97779 [article]

An incremental isomap method for hyperspectral dimensionality reduction and classification / Yi Ma in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 6 (June 2021)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 6 (June 2021) . - pp 445 - 455 Titre : An incremental isomap method for hyperspectral dimensionality reduction and classification Type de document : Article/Communication Auteurs : Yi Ma, Auteur ; Zezhong Zheng, Auteur ; Yutang Ma, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 445 - 455 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme d'apprentissage [Termes IGN] classification barycentrique [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] échantillonnage de données [Termes IGN] image AVIRIS [Termes IGN] image hyperspectrale [Termes IGN] squelettisation [Termes IGN] utilisation du sol Résumé : (Auteur) Many manifold learning algorithms conduct an eigen vector analysis on a data-similarity matrix with a size of N×N, where N is the number of data points. Thus, the memory complexity of the analysis is no less than O(N2). We present in this article an incremental manifold learning approach to handle large hyperspectral data sets for land use identification. In our method, the number of dimensions for the high-dimensional hyperspectral-image data set is obtained with the training data set. A local curvature variation algorithm is utilized to sample a subset of data points as landmarks. Then a manifold skeleton is identified based on the landmarks. Our method is validated on three AVIRIS hyperspectral data sets, outperforming the comparison algorithms with a k–nearest-neighbor classifier and achieving the second best performance with support vector machine. Numéro de notice : A2021-375 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.7.445 date de publication en ligne : 01/06/2021 En ligne : https://doi.org/10.14358/PERS.87.7.445 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97829 [article]

An innovative and automated method for characterizing wood defects on trunk surfaces using high-density 3D terrestrial LiDAR data / Van-Tho Nguyen in Annals of Forest Science [en ligne], vol 78 n° 2 (June 2021)  

Public [article]  inAnnals of Forest Science [en ligne] > vol 78 n° 2 (June 2021) . - Article 32 Titre : An innovative and automated method for characterizing wood defects on trunk surfaces using high-density 3D terrestrial LiDAR data Type de document : Article/Communication Auteurs : Van-Tho Nguyen, Auteur ; Thiéry Constant, Auteur ; Francis Colin, Auteur Année de publication : 2021 Article en page(s) : Article 32 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse comparative [Termes IGN] apprentissage automatique [Termes IGN] détection d'anomalie [Termes IGN] données de terrain [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] écorce [Termes IGN] Fagus sylvatica [Termes IGN] qualité du bois [Termes IGN] Quercus sessiliflora [Termes IGN] segmentation d'image [Termes IGN] télémétrie laser terrestre [Termes IGN] tronc Résumé : (Auteur) We designed a novel method allowing to automatically detect and measure defects on the surface of trunks including branches, branch scars, and epicormics from terrestrial LiDAR data by using only high-density 3D information. We could automatically detect and measure the defects with a diameter as small as 0.5 cm on either oak (Quercus petraea (Matt.) Liebl.) or beech (Fagus sylvatica L.) trees that display either rough or smooth bark. Context : Ground-based light detection and ranging (LiDAR) technology describes standing trees with a high level of detail. This provides an opportunity to assess standing tree quality and to use this information in forest inventory. Assuming the availability of a very high level of detail, we could extract information about the surface defects, mainly inherited from past ramification and having a strong impact on wood quality. Aims : Within the general framework of the development of a computing method able to detect, identify, and quantify the defects on the trunk surface described from 3D data produced by a terrestrial LiDAR, this study focuses on the relevance of the whole process for two tree species with contrasted bark roughness (Quercus petraea (Matt.) Liebl. and Fagus sylvatica L.) in terms of detection, identification of the defects, and comparison with measurements performed manually on the bark surface. Methods : First, a segmentation algorithm detected singularities on the trunk surface. Next, a Random Forests machine learning algorithm identified the most probable defect type and allowed the elimination of false detections. Finally, we estimated the position, horizontal, and vertical dimensions of each defect from 3D data, and we compared them to those observed directly on the trunk by an operator. Results : The defects were detected and classified with a high accuracy with an average F1 score (harmonic mean of precision and recall) of 0.74. There were differences in computed and observed defect areas, but a much closer agreement for the number of defects. Conclusion : The information about the defects present on the trunk surface measured from terrestrial LiDAR data can be used in an automated procedure for grading standing trees or roundwoods. Numéro de notice : A2021-326 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-020-01022-3 date de publication en ligne : 01/04/2021 En ligne : https://doi.org/10.1007/s13595-020-01022-3 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97484 [article]

Domain adaptive transfer attack-based segmentation networks for building extraction from aerial images / Younghwan Na in IEEE Transactions on geoscience and remote sensing, vol 59 n° 6 (June 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 6 (June 2021) . - pp 5171 - 5182 Titre : Domain adaptive transfer attack-based segmentation networks for building extraction from aerial images Type de document : Article/Communication Auteurs : Younghwan Na, Auteur ; Jun Hee Kim, Auteur ; Kyungsu Lee, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 5171 - 5182 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection du bâti [Termes IGN] entropie [Termes IGN] image aérienne [Termes IGN] segmentation sémantique Résumé : (auteur) Semantic segmentation models based on convolutional neural networks (CNNs) have gained much attention in relation to remote sensing and have achieved remarkable performance for the extraction of buildings from high-resolution aerial images. However, the issue of limited generalization for unseen images remains. When there is a domain gap between the training and test data sets, the CNN-based segmentation models trained by a training data set fail to segment buildings for the test data set. In this article, we propose segmentation networks based on a domain adaptive transfer attack (DATA) scheme for building extraction from aerial images. The proposed system combines the domain transfer and the adversarial attack concepts. Based on the DATA scheme, the distribution of the input images can be shifted to that of the target images while turning images into adversarial examples against a target network. Defending adversarial examples adapted to the target domain can overcome the performance degradation due to the domain gap and increase the robustness of the segmentation model. Cross-data set experiments and ablation study are conducted for three different data sets: the Inria aerial image labeling data set, the Massachusetts building data set, and the WHU East Asia data set. Compared with the performance of the segmentation network without the DATA scheme, the proposed method shows improvements in the overall intersection over union (IoU). Moreover, it is verified that the proposed method outperforms even when compared with feature adaptation (FA) and output space adaptation (OSA). Numéro de notice : A2021-427 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3010055 date de publication en ligne : 30/07/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3010055 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97783 [article]

A high-resolution satellite DEM filtering method assisted with building segmentation / Yihui Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 6 (June 2021)  

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Multiscale cloud detection in remote sensing images using a dual convolutional neural network / Markku Luotamo in IEEE Transactions on geoscience and remote sensing, vol 59 n° 6 (June 2021)  

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Multiscale context-aware ensemble deep KELM for efficient hyperspectral image classification / Bobo Xi in IEEE Transactions on geoscience and remote sensing, vol 59 n° 6 (June 2021)  

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Learning deep semantic segmentation network under multiple weakly-supervised constraints for cross-domain remote sensing image semantic segmentation / Yansheng Li in ISPRS Journal of photogrammetry and remote sensing, vol 175 (May 2021)  

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Learning from multimodal and multitemporal earth observation data for building damage mapping / Bruno Adriano in ISPRS Journal of photogrammetry and remote sensing, vol 175 (May 2021)  

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Performance evaluation of artificial neural networks for natural terrain classification / Perpetual Hope Akwensi in Applied geomatics, vol 13 n° 1 (May 2021)  

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Semantic hierarchy emerges in deep generative representations for scene synthesis / Ceyuan Yang in International journal of computer vision, vol 129 n° 5 (May 2021)  

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A stacked dense denoising–segmentation network for undersampled tomograms and knowledge transfer using synthetic tomograms / Dimitrios Bellos in Machine Vision and Applications, vol 32 n° 3 (May 2021)  

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A novel class-specific object-based method for urban change detection using high-resolution remote sensing imagery / Ting Bai in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 4 (April 2021)  

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Parsing of urban facades from 3D point clouds based on a novel multi-view domain / Wei Wang in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 4 (April 2021)  

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