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3D target detection using dual domain attention and SIFT operator in indoor scenes / Hanshuo Zhao in The Visual Computer, vol 38 n° 11 (November 2022)  

Public [article]  inThe Visual Computer > vol 38 n° 11 (November 2022) . - pp3765 - 3774 Titre : 3D target detection using dual domain attention and SIFT operator in indoor scenes Type de document : Article/Communication Auteurs : Hanshuo Zhao, Auteur ; Dedong Yang, Auteur ; Jiankang Yu, Auteur Année de publication : 2022 Article en page(s) : pp3765 - 3774 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] attention (apprentissage automatique) [Termes IGN] détection d'objet [Termes IGN] détection de cible [Termes IGN] jeu de données [Termes IGN] objet 3D [Termes IGN] scène intérieure [Termes IGN] SIFT (algorithme) Résumé : (auteur) In a large number of real-life scenes and practical applications, 3D object detection is playing an increasingly important role. We need to estimate the position and direction of the 3D object in the real scene to complete the 3D object detection task. In this paper, we propose a new network architecture based on VoteNet to detect 3D point cloud targets. On the one hand, we use channel and spatial dual-domain attention module to enhance the features of the object to be detected while suppressing other useless features. On the other hand, the SIFT operator has scale invariance and the ability to resist occlusion and background interference. The PointSIFT module we use can capture information in different directions of point cloud in space, and is robust to shapes of different proportions, so as to better detect objects that are partially occluded. Our method is evaluated on the SUN-RGBD and ScanNet datasets of indoor scenes. The experimental results show that our method has better performance than VoteNet. Numéro de notice : A2022-840 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00371-021-02217-z Date de publication en ligne : 28/06/2021 En ligne : https://doi.org/10.1007/s00371-021-02217-z Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102042 [article]

3D LiDAR aided GNSS/INS integration fault detection, localization and integrity assessment in urban canyons / Zhipeng Wang in Remote sensing, vol 14 n° 18 (September-2 2022)  

Public [article]  inRemote sensing > vol 14 n° 18 (September-2 2022) . - n° 4641 Titre : 3D LiDAR aided GNSS/INS integration fault detection, localization and integrity assessment in urban canyons Type de document : Article/Communication Auteurs : Zhipeng Wang, Auteur ; Bo Li, Auteur ; Zhiqiang Dan, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 4641 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] canyon urbain [Termes IGN] couplage GNSS-INS [Termes IGN] détection de cible [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] erreur de positionnement [Termes IGN] filtre adaptatif [Termes IGN] intégration de données [Termes IGN] intégrité des données [Termes IGN] khi carré [Termes IGN] semis de points Résumé : (auteur) The performance of Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) integrated navigation can be severely degraded in urban canyons due to the non-line-of-sight (NLOS) signals and multipath effects. Therefore, to achieve a high-precision and robust integrated system, real-time fault detection and localization algorithms are needed to ensure integrity. Currently, the residual chi-square test is used for fault detection in the positioning domain, but it has poor sensitivity when faults disappear. Three-dimensional (3D) light detection and ranging (LiDAR) has good positioning performance in complex environments. First, a LiDAR aided real-time fault detection algorithm is proposed. A test statistic is constructed by the mean deviation of the matched targets, and a dynamic threshold is constructed by a sliding window. Second, to solve the problem that measurement noise is estimated by prior modeling with a certain error, a LiDAR aided real-time measurement noise estimation based on adaptive filter localization algorithm is proposed according to the position deviations of matched targets. Finally, the integrity of the integrated system is assessed. The error bound of integrated positioning is innovatively verified with real test data. We conduct two experiments with a vehicle going through a viaduct and a floor hole, which, represent mid and deep urban canyons, respectively. The experimental results show that in terms of fault detection, the fault could be detected in mid urban canyons and the response time of fault disappearance is reduced by 70.24% in deep urban canyons. Thus, the poor sensitivity of the residual chi-square test for fault disappearance is improved. In terms of localization, the proposed algorithm is compared with the optimal fading factor adaptive filter (OFFAF) and the extended Kalman filter (EKF). The proposed algorithm is the most effective, and the Root Mean Square Error (RMSE) in the east and north is reduced by 12.98% and 35.1% in deep urban canyons. Regarding integrity assessment, the error bound can overbound the positioning errors in deep urban canyons relative to the EKF and the mean value of the error bounds is reduced. Numéro de notice : A2022-769 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Article DOI : 10.3390/rs14184641 Date de publication en ligne : 16/09/2022 En ligne : https://doi.org/10.3390/rs14184641 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101795 [article]

Human perception evaluation system for urban streetscapes based on computer vision algorithms with attention mechanisms / Yunhao Li in Transactions in GIS, vol 26 n° 6 (September 2022)  

Public [article]  inTransactions in GIS > vol 26 n° 6 (September 2022) . - pp 2440 - 2454 Titre : Human perception evaluation system for urban streetscapes based on computer vision algorithms with attention mechanisms Type de document : Article/Communication Auteurs : Yunhao Li, Auteur ; Chunxiao Zhang, Auteur ; Chang Wang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2440 - 2454 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] attention (apprentissage automatique) [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection de cible [Termes IGN] image virtuelle [Termes IGN] modèle 3D de l'espace urbain [Termes IGN] paysage urbain [Termes IGN] segmentation d'image [Termes IGN] vision [Termes IGN] vision par ordinateur Résumé : (auteur) Virtual 3D modeling is widely implemented in urban planning and design. To evaluate urban planning modeling, based on existing computer vision models, this article aims to improve performance in the field of human perception analysis for urban street views. In this study, the PSP module extracts detailed features from recognized objects of different sizes, an attention mechanism is applied to solve the problem of large information differences in pictures, and transfer learning technology is used to expand the model to the field of virtual 3D modeling to extract more representative and universal features, similar to how humans perceive street view information. Finally, we obtain a more objective, stable, and accurate neural network model that imitates human perception. This evaluation model converges within the correct interval on the training and validation datasets compared with an evaluation of virtual modeling by a large number of people. Numéro de notice : A2022-733 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/tgis.12882 Date de publication en ligne : 15/12/2021 En ligne : https://doi.org/10.1111/tgis.12882 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101698 [article]

Modeling human–human interaction with attention-based high-order GCN for trajectory prediction / Yanyan Fang in The Visual Computer, vol 38 n° 7 (July 2022)  

Public [article]  inThe Visual Computer > vol 38 n° 7 (July 2022) . - pp 2257 - 2269 Titre : Modeling human–human interaction with attention-based high-order GCN for trajectory prediction Type de document : Article/Communication Auteurs : Yanyan Fang, Auteur ; Zhiyu Jin, Auteur ; Zhenhua Cui, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2257 - 2269 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] attention (apprentissage automatique) [Termes IGN] détection de cible [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] interaction spatiale [Termes IGN] modèle de simulation [Termes IGN] objet mobile [Termes IGN] piéton [Termes IGN] réseau neuronal de graphes [Termes IGN] trajet (mobilité) Résumé : (auteur) This paper presents a novel high-order graph convolutional network (GCN) for pedestrian trajectory prediction. Specifically, the walking state of a target pedestrian depends on both its historical trajectory, which encodes its speed, walking direction and acceleration information, as well as the movement of its neighbors. Thus we propose to leverage GCNs to aggregate the trajectory features of the target pedestrian and its neighbors to predict the movement of the target pedestrian. Considering that the movement of the neighbors’ neighbors affects the movement of the target pedestrian’s neighbors, thus indirectly affecting the movement of the target pedestrian, we propose to use a high-order GCN for human–human interaction modelling. Such a high-order GCN considers the target pedestrian’s neighbors as well as its neighbors’ neighbors. Further, a pedestrian avoids collision with others by estimating its locations and its neighbors’ upcoming locations, and it slows down or changes direction if it believes a collision may occur, especially in very crowded scenes. In light of this, we propose to model such anticipation-based decision making behavior as attention and combine it with our high-order GCN. Thus we first roughly estimate the future trajectories of all pedestrians with a simple method. By using the coarse predicted future trajectory and GCN outputs, we calculate the attention in our attention-based high-order GCN and predict future trajectory. Extensive experiments validate the effectiveness of our approach. In addition, our model shows a higher data efficiency. On the ETH&UCY dataset, using only 5% of the training data for each training epoch, our model outperforms the state of the art. Numéro de notice : A2022-507 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00371-021-02109-2 Date de publication en ligne : 01/07/2021 En ligne : https://doi.org/10.1007/s00371-021-02109-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101040 [article]

A new method to detect targets in hyperspectral images based on principal component analysis / Shahram Sharifi Hashjin in Geocarto international, vol 37 n° 9 ([15/05/2022])  

Public [article]  inGeocarto international > vol 37 n° 9 [15/05/2022] . - pp 2679 - 2697 Titre : A new method to detect targets in hyperspectral images based on principal component analysis Type de document : Article/Communication Auteurs : Shahram Sharifi Hashjin, Auteur ; Safa Khazai, Auteur Année de publication : 2022 Article en page(s) : pp 2679 - 2697 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse de groupement [Termes IGN] analyse des mélanges spectraux [Termes IGN] analyse en composantes principales [Termes IGN] détection de cible [Termes IGN] estimation de cohérence [Termes IGN] extraction de traits caractéristiques [Termes IGN] image hyperspectrale Résumé : (auteur) Target detection (TD) is a major task in hyperspectral image (HSI) processing which, due to the high spectral resolution, requires dealing with the curse of dimensionality. The integrated feature extraction and selection is a well-known solution for dimensionality reduction of HSIs. In this study, a new method is presented to improve the performance of TD algorithms based on principal component analysis (PCA) feature space. In this method, using the implantation of the target spectrum (TS) in the HSI and following the simulated targets in the PCA feature space, the best principal components (PCs) are selected. Then, using the mixing and unmixing coefficients of the PCs, a new TS and a new image in the PCA feature space are created. Afterwards, using the new spectrum of the target, the TD algorithm is run on the new HSI. The performance of the proposed method is compared to nine counterpart algorithms on Hymap and Hyperion HSI. All the comparisons are performed using adaptive coherence estimator (ACE) TD algorithm. Experimental results illustrate that the proposed method, compared to its counterparts, yields superior performance based on the false alarm rate (FAR) measure. It gives an average FAR value of about 16, which is approximately 9% better than that of its best counterparts. Numéro de notice : A2022-568 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1831625 Date de publication en ligne : 01/12/2020 En ligne : https://doi.org/10.1080/10106049.2020.1831625 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101251 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2022091	RAB	Revue	Centre de documentation	En réserve L003	Disponible

The effect of map label language on the visual search of cartographic point symbols / Paweł Cybulski in Cartography and Geographic Information Science, vol 49 n° 3 (May 2022)  

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Meta-learning based hyperspectral target detection using siamese network / Yulei Wang in IEEE Transactions on geoscience and remote sensing, vol 60 n° 4 (April 2022)  

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Automatic extraction of damaged houses by earthquake based on improved YOLOv5: A case study in Yangbi / Yafei Jing in Remote sensing, vol 14 n° 2 (January-2 2022)  

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Détection des micro et macroplastiques à partir de mesures spectrales / Martin Cubaud (2022) 

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STC-Det: A slender target detector combining shadow and target information in optical satellite images / Zhaoyang Huang in Remote sensing, vol 13 n° 20 (October-2 2021)  

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Coniferous and broad-leaved forest distinguishing using L-band polarimetric SAR data / Fang Shang in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 9 (September 2021)  

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Vehicle detection in very-high-resolution remote sensing images based on an anchor-free detection model with a more precise foveal area / Xungen Li in ISPRS International journal of geo-information, vol 10 n° 8 (August 2021)  

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Target-constrained interference-minimized band selection for hyperspectral target detection / Xiaodi Shang in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 7 (July 2021)  

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Robust detection of non-overlapping ellipses from points with applications to circular target extraction in images and cylinder detection in point clouds / Reza Maalek in ISPRS Journal of photogrammetry and remote sensing, vol 176 (June 2021)  

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Lifting scheme-based sparse density feature extraction for remote sensing target detection / Ling Tian in Remote sensing, vol 13 n° 9 (May-1 2021)  

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