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Robust registration of aerial images and LiDAR data using spatial constraints and Gabor structural features / Bai Zhu in ISPRS Journal of photogrammetry and remote sensing, Vol 181 (November 2021)
[article]
Titre : Robust registration of aerial images and LiDAR data using spatial constraints and Gabor structural features Type de document : Article/Communication Auteurs : Bai Zhu, Auteur ; Yuanxin Ye, Auteur ; Liang Zhou, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 129 - 147 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] algorithme ICP
[Termes IGN] correction géométrique
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] élément d'orientation externe
[Termes IGN] enregistrement de données
[Termes IGN] filtre de Gabor
[Termes IGN] image aérienne
[Termes IGN] recalage d'image
[Termes IGN] semis de points
[Termes IGN] SIFT (algorithme)
[Termes IGN] structure-from-motionRésumé : (auteur) Co-registration of aerial imagery and Light Detection and Ranging (LiDAR) data is quite challenging because the different imaging mechanisms produce significant geometric and radiometric distortions between the two multimodal data sources. To address this problem, we propose a robust and effective coarse-to-fine registration method that is conducted in two stages utilizing spatial constraints and Gabor structural features. In the first stage, the LiDAR point cloud data is transformed into an intensity map that is used as the reference image. Then, coarse registration is completed by designing a partition-based Features from Accelerated Segment Test (FAST) operator to extract the uniformly distributed interest points in the aerial images and thereafter performing a local geometric correction based on the collinearity equations using the exterior orientation parameters (EoPs). The coarse registration aims to provide a reliable spatial geometry relationship for the subsequent fine registration and is designed to eliminate rotation and scale changes, as well as making only a few translation differences exist between the images. In the second stage, a novel feature descriptor called multi-Scale and multi-Directional Features of odd Gabor (SDFG) is first built to capture the multi-scale and multi-directional structural properties of the images. Then, the three-dimensional (3D) phase correlation (PC) of the SDFG descriptor is established to detect the control points (CPs) between the aerial and LiDAR intensity image in the frequency domain, where the image matching is accelerated by the 3D Fast Fourier Transform (FFT) technique. Finally, the obtained CPs not only are employed to refine the EoPs, but also are used to achieve the fine registration of the aerial images and LiDAR data. We conduct experiments to verify the robustness of the proposed registration method using three sets of aerial images and LiDAR data with different scene coverage. Experimental results show that the proposed method is robust to geometric distortions and radiometric changes. Moreover, it achieves the registration accuracy of less than 2 pixels for all cases, which outperforms the current four state-of-the-art methods, demonstrating its superior registration performance. Numéro de notice : A2021-773 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.09.010 Date de publication en ligne : 21/09/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.09.010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98830
in ISPRS Journal of photogrammetry and remote sensing > Vol 181 (November 2021) . - pp 129 - 147[article]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)
[article]
Titre : STC-Det: A slender target detector combining shadow and target information in optical satellite images Type de document : Article/Communication Auteurs : Zhaoyang Huang, Auteur ; Feng Wang, Auteur ; Hongjian You, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 4183 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] appariement automatique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection d'objet
[Termes IGN] détection de cible
[Termes IGN] fusion de données
[Termes IGN] image satellite
[Termes IGN] ombreRésumé : (auteur) Object detection has made great progress. However, due to the unique imaging method of optical satellite remote sensing, the detection of slender targets is still insufficient. Specifically, the perspective of optical satellites is small, and the characteristics of slender targets are severely lost during imaging, resulting in insufficient detection task information; at the same time, the appearance of slender targets in the image is greatly affected by the satellite perspective, which is likely to cause insufficient generalization capabilities of conventional detection models. In response to these two points, we have made some improvements. First, in this paper, we introduce the shadow as auxiliary information to complement the trunk features of the target lost in imaging. Second, to reduce the impact of satellite perspective on imaging, in this paper, we use the characteristic that shadow information is not affected by satellite perspective to design STC-Det. STC-Det treats the shadow and the target as two different types of targets and uses the shadow information to assist the detection, reducing the impact of the satellite perspective on detection. Among them, in order to improve the performance of STC-Det, we propose an automatic matching method (AMM) of shadow and target and a feature fusion method (FFM). Finally, this paper proposes a new method to calculate the heatmaps of detectors, which verifies the effectiveness of the proposed network in a visual way. Experiments show that when the satellite perspective is variable, the precision of STC-Det is increased by 1.7%, and when the satellite perspective is small, the precision of STC-Det is increased by 5.2%. Numéro de notice : A2021-804 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13204183 Date de publication en ligne : 19/10/2021 En ligne : https://doi.org/10.3390/rs13204183 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98860
in Remote sensing > vol 13 n° 20 (October-2 2021) . - n° 4183[article]Disaster Image Classification by Fusing Multimodal Social Media Data / Zhiqiang Zou in ISPRS International journal of geo-information, vol 10 n° 10 (October 2021)
[article]
Titre : Disaster Image Classification by Fusing Multimodal Social Media Data Type de document : Article/Communication Auteurs : Zhiqiang Zou, Auteur ; Hongyu Gan, Auteur ; Qunying Huang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 636 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] analyse visuelle
[Termes IGN] apprentissage profond
[Termes IGN] catastrophe naturelle
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] corpus
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] données localisées des bénévoles
[Termes IGN] données massives
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] fusion de données multisource
[Termes IGN] qualité des données
[Termes IGN] traitement de donnéesNuméro de notice : A2021-803 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10100636 Date de publication en ligne : 24/09/2021 En ligne : https://doi.org/10.3390/ijgi10100636 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98856
in ISPRS International journal of geo-information > vol 10 n° 10 (October 2021) . - n° 636[article]Utilisation de l'apprentissage profond dans la modélisation 3D urbaine [Partie 1] / Hamza Ben Addou in Géomatique expert, n° 135 (septembre 2021)
[article]
Titre : Utilisation de l'apprentissage profond dans la modélisation 3D urbaine [Partie 1] Type de document : Article/Communication Auteurs : Hamza Ben Addou, Auteur Année de publication : 2021 Article en page(s) : pp 11 - 20 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection du bâti
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] emprise au sol
[Termes IGN] fusion de données multisource
[Termes IGN] image aérienne
[Termes IGN] information sémantique
[Termes IGN] modèle 3D de l'espace urbain
[Termes IGN] segmentation d'image
[Termes IGN] semis de pointsRésumé : (Auteur) Partie 1 : Mise en place d’un processus de détection automatique des emprises de bâtiments par apprentissage profond Numéro de notice : A2021-660 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE/URBANISME Nature : Article nature-HAL : ArtSansCL DOI : sans Date de publication en ligne : 01/09/2021 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98414
in Géomatique expert > n° 135 (septembre 2021) . - pp 11 - 20[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité IFN-001-P002273 PER Revue Nogent-sur-Vernisson Salle périodiques Exclu du prêt Improving urban land cover classification with combined use of Sentinel-2 and Sentinel-1 imagery / Bin Hu in ISPRS International journal of geo-information, vol 10 n° 8 (August 2021)
[article]
Titre : Improving urban land cover classification with combined use of Sentinel-2 and Sentinel-1 imagery Type de document : Article/Communication Auteurs : Bin Hu, Auteur ; Yongyang Xu, Auteur ; Xiao Huang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 533 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Chine
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] fusion de données
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] occupation du sol
[Termes IGN] planification urbaine
[Termes IGN] polarisation
[Termes IGN] zone urbaineRésumé : (auteur) Accurate land cover mapping is important for urban planning and management. Remote sensing data have been widely applied for urban land cover mapping. However, obtaining land cover classification via optical remote sensing data alone is difficult due to spectral confusion. To reduce the confusion between dark impervious surface and water, the Sentinel-1A Synthetic Aperture Rader (SAR) data are synergistically combined with the Sentinel-2B Multispectral Instrument (MSI) data. The novel support vector machine with composite kernels (SVM-CK) approach, which can exploit the spatial information, is proposed to process the combination of Sentinel-2B MSI and Sentinel-1A SAR data. The classification based on the fusion of Sentinel-2B and Sentinel-1A data yields an overall accuracy (OA) of 92.12% with a kappa coefficient (KA) of 0.89, superior to the classification results using Sentinel-2B MSI imagery and Sentinel-1A SAR imagery separately. The results indicate that the inclusion of Sentinel-1A SAR data to Sentinel-2B MSI data can improve the classification performance by reducing the confusion between built-up area and water. This study shows that the land cover classification can be improved by fusing Sentinel-2B and Sentinel-1A imagery. Numéro de notice : A2021-590 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10080533 Date de publication en ligne : 09/08/2021 En ligne : https://doi.org/10.3390/ijgi10080533 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98210
in ISPRS International journal of geo-information > vol 10 n° 8 (August 2021) . - n° 533[article]CNN-based RGB-D salient object detection: Learn, select, and fuse / Hao Chen in International journal of computer vision, vol 129 n° 7 (July 2021)PermalinkAn area merging method in map generalization considering typical characteristics of structured geographic objects / Chengming Li in Cartography and Geographic Information Science, vol 48 n° 3 (May 2021)PermalinkAn improved computerized ionospheric tomography model fusing 3-D multisource ionospheric data enabled quantifying the evolution of magnetic storm / Jian Kong in IEEE Transactions on geoscience and remote sensing, vol 59 n° 5 (May 2021)PermalinkIntegration of laser scanner and photogrammetry for heritage BIM enhancement / Yahya Alshawabkeh in ISPRS International journal of geo-information, vol 10 n° 5 (May 2021)PermalinkParallel computing for fast spatiotemporal weighted regression / Xiang Que in Computers & geosciences, vol 150 (May 2021)PermalinkA data fusion-based framework to integrate multi-source VGI in an authoritative land use database / Lanfa Liu in International Journal of Digital Earth, vol 14 n° 4 (April 2021)PermalinkA geographic information-driven method and a new large scale dataset for remote sensing cloud/snow detection / Xi Wu in ISPRS Journal of photogrammetry and remote sensing, vol 174 (April 2021)PermalinkParsing 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)PermalinkPrecipitable water vapor fusion based on a generalized regression neural network / Bao Zhang in Journal of geodesy, vol 95 n° 4 (April 2021)PermalinkAutomated registration of SfM‐MVS multitemporal datasets using terrestrial and oblique aerial images / Luigi Parente in Photogrammetric record, vol 36 n° 173 (March 2021)PermalinkImproving trajectory estimation using 3D city models and kinematic point clouds / Lucas Lucks in Transactions in GIS, Vol 25 n° 1 (February 2021)PermalinkPermalinkPermalinkCartographie dense et compacte par vision RGB-D pour la navigation d’un robot mobile / Bruce Canovas (2021)PermalinkDeep convolutional neural networks for scene understanding and motion planning for self-driving vehicles / Abdelhak Loukkal (2021)PermalinkIntelligent sensors for positioning, tracking, monitoring, navigation and smart sensing in smart cities / Li Tiancheng (2021)PermalinkMéthodes de partage d'informations visuelles et inertielles pour la localisation et la cartographie simultanées décentralisées multi-robots / Rodolphe Dubois (2021)PermalinkModélisation et raisonnement spatial flou pour l’aide à la localisation de victimes en montagne / Mattia Bunel (2021)PermalinkModélisation et reconstitution 3D de vestiges du Struthof en relation avec le PCR à partir d’éléments historiques / Yassine Seddik (2021)PermalinkPermalink