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DART-Lux: An unbiased and rapid Monte Carlo radiative transfer method for simulating remote sensing images / Yingjie Wang in Remote sensing of environment, vol 274 (June 2022)
[article]
Titre : DART-Lux: An unbiased and rapid Monte Carlo radiative transfer method for simulating remote sensing images Type de document : Article/Communication Auteurs : Yingjie Wang, Auteur ; Abdelaziz Kallel, Auteur ; Xuebo Yang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 112973 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] bande spectrale
[Termes IGN] distribution du coefficient de réflexion bidirectionnelle BRDF
[Termes IGN] image à haute résolution
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] modèle de transfert radiatif
[Termes IGN] radiance
[Termes IGN] réflectance directionnelle
[Termes IGN] scène forestière
[Termes IGN] scène urbaineRésumé : (auteur) Accurate and efficient simulation of remote sensing images is increasingly needed in order to better exploit remote sensing observations and to better design remote sensing missions. DART (Discrete Anisotropic Radiative Transfer), developed since 1992 based on the discrete ordinates method (i.e., standard mode DART-FT), is one of the most accurate and comprehensive 3D radiative transfer models to simulate the radiative budget and remote sensing observations of urban and natural landscapes. Recently, a new method, called DART-Lux, was integrated into DART model to address the requirements of massive remote sensing data simulation for large-scale and complex landscapes. It is developed based on efficient Monte Carlo light transport algorithms (i.e., bidirectional path tracing) and on DART model framework. DART-Lux can accurately and rapidly simulate the bidirectional reflectance factor (BRF) and spectral images of arbitrary landscapes. This paper presents its theory, implementation, and evaluation. Its accuracy, efficiency and advantages are also discussed. The comparison with standard DART-FT in a variety of scenarios shows that DART-Lux is consistent with DART-FT (relative differences Numéro de notice : A2022-398 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2022.112973 Date de publication en ligne : 26/03/2022 En ligne : https://doi.org/10.1016/j.rse.2022.112973 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100698
in Remote sensing of environment > vol 274 (June 2022) . - n° 112973[article]HyperNet: A deep network for hyperspectral, multispectral, and panchromatic image fusion / Kun Li in ISPRS Journal of photogrammetry and remote sensing, vol 188 (June 2022)
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Titre : HyperNet: A deep network for hyperspectral, multispectral, and panchromatic image fusion Type de document : Article/Communication Auteurs : Kun Li, Auteur ; Wei Zhang, Auteur ; Dian Yu, Auteur ; Xin Tian, Auteur Année de publication : 2022 Article en page(s) : pp 30 - 44 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] fusion d'images
[Termes IGN] image à haute résolution
[Termes IGN] image floue
[Termes IGN] image hyperspectrale
[Termes IGN] image multibande
[Termes IGN] image panchromatique
[Termes IGN] pansharpening (fusion d'images)
[Termes IGN] réseau neuronal profondRésumé : (Auteur) Traditional approaches mainly fuse a hyperspectral image (HSI) with a high-resolution multispectral image (MSI) to improve the spatial resolution of the HSI. However, such improvement in the spatial resolution of HSIs is still limited because the spatial resolution of MSIs remains low. To further improve the spatial resolution of HSIs, we propose HyperNet, a deep network for the fusion of HSI, MSI, and panchromatic image (PAN), which effectively injects the spatial details of an MSI and a PAN into an HSI while preserving the spectral information of the HSI. Thus, we design HyperNet on the basis of a uniform fusion strategy to solve the problem of complex fusion of three types of sources (i.e., HSI, MSI, and PAN). In particular, the spatial details of the MSI and the PAN are extracted by multiple specially designed multiscale-attention-enhance blocks in which multi-scale convolution is used to adaptively extract features from different reception fields, and two attention mechanisms are adopted to enhance the representation capability of features along the spectral and spatial dimensions, respectively. Through the capability of feature reuse and interaction in a specially designed dense-detail-insertion block, the previously extracted features are subsequently injected into the HSI according to the unidirectional feature propagation among the layers of dense connection. Finally, we construct an efficient loss function by integrating the multi-scale structural similarity index with the norm, which drives HyperNet to generate high-quality results with a good balance between spatial and spectral qualities. Extensive experiments on simulated and real data sets qualitatively and quantitatively demonstrate the superiority of HyperNet over other state-of-the-art methods. Numéro de notice : A2022-272 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.04.001 Date de publication en ligne : 07/04/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.04.001 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100461
in ISPRS Journal of photogrammetry and remote sensing > vol 188 (June 2022) . - pp 30 - 44[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2022061 SL Revue Centre de documentation Revues en salle Disponible 081-2022063 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2022062 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Precise crop classification of hyperspectral images using multi-branch feature fusion and dilation-based MLP / Haibin Wu in Remote sensing, vol 14 n° 11 (June-1 2022)
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Titre : Precise crop classification of hyperspectral images using multi-branch feature fusion and dilation-based MLP Type de document : Article/Communication Auteurs : Haibin Wu, Auteur ; Huaming Zhou, Auteur ; Aili Wang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 2713 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse en composantes principales
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] cultures
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image hyperspectrale
[Termes IGN] Perceptron multicoucheRésumé : (auteur) The precise classification of crop types using hyperspectral remote sensing imaging is an essential application in the field of agriculture, and is of significance for crop yield estimation and growth monitoring. Among the deep learning methods, Convolutional Neural Networks (CNNs) are the premier model for hyperspectral image (HSI) classification for their outstanding locally contextual modeling capability, which facilitates spatial and spectral feature extraction. Nevertheless, the existing CNNs have a fixed shape and are limited to observing restricted receptive fields, constituting a simulation difficulty for modeling long-range dependencies. To tackle this challenge, this paper proposed two novel classification frameworks which are both built from multilayer perceptrons (MLPs). Firstly, we put forward a dilation-based MLP (DMLP) model, in which the dilated convolutional layer replaced the ordinary convolution of MLP, enlarging the receptive field without losing resolution and keeping the relative spatial position of pixels unchanged. Secondly, the paper proposes multi-branch residual blocks and DMLP concerning performance feature fusion after principal component analysis (PCA), called DMLPFFN, which makes full use of the multi-level feature information of the HSI. The proposed approaches are carried out on two widely used hyperspectral datasets: Salinas and KSC; and two practical crop hyperspectral datasets: WHU-Hi-LongKou and WHU-Hi-HanChuan. Experimental results show that the proposed methods outshine several state-of-the-art methods, outperforming CNN by 6.81%, 12.45%, 4.38% and 8.84%, and outperforming ResNet by 4.48%, 7.74%, 3.53% and 6.39% on the Salinas, KSC, WHU-Hi-LongKou and WHU-Hi-HanChuan datasets, respectively. As a result of this study, it was confirmed that the proposed methods offer remarkable performances for hyperspectral precise crop classification. Numéro de notice : A2022-539 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14112713 Date de publication en ligne : 05/06/2022 En ligne : https://doi.org/10.3390/rs14112713 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101102
in Remote sensing > vol 14 n° 11 (June-1 2022) . - n° 2713[article]Summarizing large scale 3D mesh for urban navigation / Imeen Ben Salah in Robotics and autonomous systems, vol 152 (June 2022)
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Titre : Summarizing large scale 3D mesh for urban navigation Type de document : Article/Communication Auteurs : Imeen Ben Salah, Auteur ; Sébastien Kramm, Auteur ; Cédric Demonceaux, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 104037 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme ICP
[Termes IGN] carte en 3D
[Termes IGN] données lidar
[Termes IGN] entropie
[Termes IGN] image hémisphérique
[Termes IGN] image RVB
[Termes IGN] information sémantique
[Termes IGN] localisation basée vision
[Termes IGN] maillage
[Termes IGN] navigation autonome
[Termes IGN] précision géométrique (imagerie)
[Termes IGN] précision radiométrique
[Termes IGN] profondeur
[Termes IGN] Rouen
[Termes IGN] saillance
[Termes IGN] zone urbaineRésumé : (auteur) Cameras have become increasingly common in vehicles, smartphones, and advanced driver assistance systems. The areas of application of these cameras in the world of intelligent transportation systems are becoming more and more varied: pedestrian detection, line crossing detection, navigation, …A major area of research currently focuses on mapping that is essential for localization and navigation. However, this step generates an important problem of memory management. Indeed, the memory space required to accommodate the map of a small city is measured in tens gigabytes. In addition, several providers today are competing to produce High-Definition (HD) maps. These maps offer a rich and detailed representation of the environment for highly accurate localization. However, they require a large storage capacity and high transmission and update costs. To overcome these problems, we propose a solution to summarize this type of map by reducing the size while maintaining the relevance of the data for navigation based on vision only. The summary consists in a set of spherical images augmented by depth and semantic information and allowing to keep the same level of visibility in every directions. These spheres are used as landmarks to offer guidance information to a distant agent. They then have to guarantee, at a lower cost, a good level of precision and speed during navigation. Some experiments on real data demonstrate the feasibility for obtaining a summarized map while maintaining a localization with interesting performances. Numéro de notice : A2022-290 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.robot.2022.104037 Date de publication en ligne : 03/02/2022 En ligne : https://doi.org/10.1016/j.robot.2022.104037 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100335
in Robotics and autonomous systems > vol 152 (June 2022) . - n° 104037[article]True orthophoto generation based on unmanned aerial vehicle images using reconstructed edge points / Mojdeh Ebrahimikia in Photogrammetric record, vol 37 n° 178 (June 2022)
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Titre : True orthophoto generation based on unmanned aerial vehicle images using reconstructed edge points Type de document : Article/Communication Auteurs : Mojdeh Ebrahimikia, Auteur ; Ali Hosseininaveh, Auteur Année de publication : 2022 Article en page(s) : pp 161 - 184 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie
[Termes IGN] apprentissage profond
[Termes IGN] détection de contours
[Termes IGN] détection du bâti
[Termes IGN] distorsion d'image
[Termes IGN] graphe
[Termes IGN] image captée par drone
[Termes IGN] modèle numérique de surface
[Termes IGN] orthophotographie
[Termes IGN] orthophotoplan numérique
[Termes IGN] photogrammétrie aérienne
[Termes IGN] pixel de contour
[Termes IGN] semis de points
[Termes IGN] structure-from-motion
[Termes IGN] zone urbaineRésumé : (auteur) After considering state-of-the-art algorithms, this paper presents a novel method for generating true orthophotos from unmanned aerial vehicle (UAV) images of urban areas. The procedure consists of four steps: 2D edge detection in building regions, 3D edge graph generation, digital surface model (DSM) modification and, finally, true orthophoto and orthomosaic generation. The main contribution of this paper is concerned with the first two steps, in which deep-learning approaches are used to identify the structural edges of the buildings and the estimated 3D edge points are added to the point cloud for DSM modification. Running the proposed method as well as four state-of-the-art methods on two different datasets demonstrates that the proposed method outperforms the existing orthophoto improvement methods by up to 50% in the first dataset and by 70% in the second dataset by reducing true orthophoto distortion in the structured edges of the buildings. Numéro de notice : A2022-517 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12409 Date de publication en ligne : 05/04/2022 En ligne : https://doi.org/10.1111/phor.12409 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101065
in Photogrammetric record > vol 37 n° 178 (June 2022) . - pp 161 - 184[article]Projective multitexturing of current 3D city models and point clouds with many historical images / Maria Scarlleth Gomes de Castro in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-4-2022 (2022 edition)PermalinkApplication oriented quality evaluation of Gaofen-7 optical stereo satellite imagery / Jiaojiao Tian in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-1-2022 (2022 edition)PermalinkCalibration of a light hemispherical radiance field imaging system / Manchun Lei in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-1-2022 (2022 edition)PermalinkCooperative image orientation considering dynamic objects / P. Trusheim in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-1-2022 (2022 edition)PermalinkRailway lidar semantic segmentation with axially symmetrical convolutional learning / Antoine Manier in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)PermalinkSemantic segmentation of urban textured meshes through point sampling / Grégoire Grzeczkowicz in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)PermalinkVegetation cover mapping from RGB webcam time series for land surface emissivity retrieval in high mountain areas / Benedikt Hiebl in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)PermalinkVirtual laser scanning of dynamic scenes created from real 4D topographic point cloud data / Lukas Winiwarter in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)PermalinkA 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])PermalinkResearch on automatic identification method of terraces on the Loess plateau based on deep transfer learning / Mingge Yu in Remote sensing, vol 14 n° 10 (May-2 2022)Permalink