Centre de documentation
scientifique

Investigating the ability to identify new constructions in urban areas using images from unmanned aerial vehicles, Google Earth, and Sentinel-2 / Fahime Arabi Aliabad in Remote sensing, vol 14 n° 13 (July-1 2022)  

Public [article]  inRemote sensing > vol 14 n° 13 (July-1 2022) . - n° 3227 Titre : Investigating the ability to identify new constructions in urban areas using images from unmanned aerial vehicles, Google Earth, and Sentinel-2 Type de document : Article/Communication Auteurs : Fahime Arabi Aliabad, Auteur ; Hamid Reza Ghafarian Malamiri, Auteur ; Saeed Shojaei, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 3227 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification orientée objet [Termes IGN] croissance urbaine [Termes IGN] détection de changement [Termes IGN] Google Earth [Termes IGN] image captée par drone [Termes IGN] image Sentinel-MSI [Termes IGN] utilisation du sol [Termes IGN] zone urbaine Résumé : (auteur) One of the main problems in developing countries is unplanned urban growth and land use change. Timely identification of new constructions can be a good solution to mitigate some environmental and social problems. This study examined the possibility of identifying new constructions in urban areas using images from unmanned aerial vehicles (UAV), Google Earth and Sentinel-2. The accuracy of the land cover map obtained using these images was investigated using pixel-based processing methods (maximum likelihood, minimum distance, Mahalanobis, spectral angle mapping (SAM)) and object-based methods (Bayes, support vector machine (SVM), K-nearest-neighbor (KNN), decision tree, random forest). The use of DSM to increase the accuracy of classification of UAV images and the use of NDVI to identify vegetation in Sentinel-2 images were also investigated. The object-based KNN method was found to have the greatest accuracy in classifying UAV images (kappa coefficient = 0.93), and the use of DSM increased the classification accuracy by 4%. Evaluations of the accuracy of Google Earth images showed that KNN was also the best method for preparing a land cover map using these images (kappa coefficient = 0.83). The KNN and SVM methods showed the highest accuracy in preparing land cover maps using Sentinel-2 images (kappa coefficient = 0.87 and 0.85, respectively). The accuracy of classification was not increased when using NDVI due to the small percentage of vegetation cover in the study area. On examining the advantages and disadvantages of the different methods, a novel method for identifying new rural constructions was devised. This method uses only one UAV imaging per year to determine the exact position of urban areas with no constructions and then examines spectral changes in related Sentinel-2 pixels that might indicate new constructions in these areas. On-site observations confirmed the accuracy of this method. Numéro de notice : A2022-572 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article DOI : 10.3390/rs14133227 Date de publication en ligne : 05/07/2022 En ligne : https://doi.org/10.3390/rs14133227 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101288 [article]

Investigating the role of image retrieval for visual localization / Martin Humenberger in International journal of computer vision, vol 130 n° 7 (July 2022)  

Public [article]  inInternational journal of computer vision > vol 130 n° 7 (July 2022) . - 1811 - 1836 Titre : Investigating the role of image retrieval for visual localization Type de document : Article/Communication Auteurs : Martin Humenberger, Auteur ; Yohann Cabon, Auteur ; Noé Pion, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : 1811 - 1836 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse visuelle [Termes IGN] base de données d'images [Termes IGN] estimation de pose [Termes IGN] flou [Termes IGN] localisation basée image [Termes IGN] localisation basée vision [Termes IGN] point de repère [Termes IGN] précision de localisation [Termes IGN] Ransac (algorithme) [Termes IGN] réalité de terrain [Termes IGN] structure-from-motion [Termes IGN] vision par ordinateur Résumé : (auteur) Visual localization, i.e., camera pose estimation in a known scene, is a core component of technologies such as autonomous driving and augmented reality. State-of-the-art localization approaches often rely on image retrieval techniques for one of two purposes: (1) provide an approximate pose estimate or (2) determine which parts of the scene are potentially visible in a given query image. It is common practice to use state-of-the-art image retrieval algorithms for both of them. These algorithms are often trained for the goal of retrieving the same landmark under a large range of viewpoint changes which often differs from the requirements of visual localization. In order to investigate the consequences for visual localization, this paper focuses on understanding the role of image retrieval for multiple visual localization paradigms. First, we introduce a novel benchmark setup and compare state-of-the-art retrieval representations on multiple datasets using localization performance as metric. Second, we investigate several definitions of “ground truth” for image retrieval. Using these definitions as upper bounds for the visual localization paradigms, we show that there is still significant room for improvement. Third, using these tools and in-depth analysis, we show that retrieval performance on classical landmark retrieval or place recognition tasks correlates only for some but not all paradigms to localization performance. Finally, we analyze the effects of blur and dynamic scenes in the images. We conclude that there is a need for retrieval approaches specifically designed for localization paradigms. Our benchmark and evaluation protocols are available at https://github.com/naver/kapture-localization. Numéro de notice : A2022-538 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s11263-022-01615-7 Date de publication en ligne : 25/05/2022 En ligne : https://doi.org/10.1007/s11263-022-01615-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101070 [article]

Lidar point-to-point correspondences for rigorous registration of kinematic scanning in dynamic networks / Aurélien Brun in ISPRS Journal of photogrammetry and remote sensing, vol 189 (July 2022)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 189 (July 2022) . - pp 185 - 200 Titre : Lidar point-to-point correspondences for rigorous registration of kinematic scanning in dynamic networks Type de document : Article/Communication Auteurs : Aurélien Brun, Auteur ; Davide Antonio Cucci, Auteur ; Jan Skaloud, Auteur Année de publication : 2022 Article en page(s) : pp 185 - 200 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] appariement de points [Termes IGN] centrale inertielle [Termes IGN] données lidar [Termes IGN] filtre de Kalman [Termes IGN] géoréférencement [Termes IGN] précision du positionnement [Termes IGN] Ransac (algorithme) [Termes IGN] semis de points [Termes IGN] signal GNSS [Termes IGN] superposition Résumé : (auteur) With the objective of improving the registration of lidar point clouds produced by kinematic scanning systems, we propose a novel trajectory adjustment procedure that leverages on the automated extraction of selected reliable 3D point–to–point correspondences between overlapping point clouds and their joint integration (adjustment) together with raw inertial and GNSS observations. This is performed in a tightly coupled fashion using a dynamic network approach that results in an optimally compensated trajectory through modeling of errors at the sensor, rather than the trajectory, level. The 3D correspondences are formulated as static conditions within the dynamic network and the registered point cloud is generated with significantly higher accuracy based on the corrected trajectory and possibly other parameters determined within the adjustment. We first describe the method for selecting correspondences and how they are inserted into the dynamic network via new observation model while providing an open-source implementation of the solver employed in this work. We then describe the experiments conducted to evaluate the performance of the proposed framework in practical airborne laser scanning scenarios with low-cost MEMS inertial sensors. In the conducted experiments, the method proposed to establish 3D correspondences is effective in determining point–to–point matches across a wide range of geometries such as trees, buildings and cars. Our results demonstrate that the method improves the point cloud registration accuracy (5 in nominal and 10 in emulated GNSS outage conditions within the studied cases), which is otherwise strongly affected by errors in the determined platform attitude or position, and possibly determine unknown boresight angles. The proposed methods remain effective even if only a fraction (0.1%) of the total number of established 3D correspondences are considered in the adjustment. Numéro de notice : A2022-413 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.04.027 Date de publication en ligne : 19/05/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.04.027 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100764 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2022071	SL	Revue	Centre de documentation	Revues en salle	Disponible

Quantifying the influence of plot-level uncertainty in above ground biomass up scaling using remote sensing data in central Indian dry deciduous forest / Thangavelu Mayamanikandan in Geocarto international, vol 37 n° 12 ([01/07/2022])  

Public [article]  inGeocarto international > vol 37 n° 12 [01/07/2022] . - pp 3489 - 3503 Titre : Quantifying the influence of plot-level uncertainty in above ground biomass up scaling using remote sensing data in central Indian dry deciduous forest Type de document : Article/Communication Auteurs : Thangavelu Mayamanikandan, Auteur ; Suraj Reddy, Auteur ; Rakesh Fararoda, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 3489 - 3503 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] biomasse aérienne [Termes IGN] forêt de feuillus [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR [Termes IGN] incertitude des données [Termes IGN] Inde [Termes IGN] placette d'échantillonnage [Termes IGN] superposition d'images Résumé : (auteur) Accurate and reliable estimation of Above Ground Biomass (AGB) in tropical forests is much needed for net carbon assessments. The aim of the study is to determine the uncertainty in biomass estimation in terms of field plot size, shape and location error using field plot and remote sensing data in tropical dry deciduous forests of India. Detailed tree measurements and location mapping are performed in 13 (1 ha) plots and 1 a very large permanent plot of 32 ha and AGB is estimated using local volume equations. Remote sensing-based AGB estimated using a multiple linear regression model between the reflectance (Sentinel-2) and backscatter (Sentinel-1) with field AGB. The result shows relative root mean square error of the model decreased by approximately 50% with a plot size increase from 0.01 ha (64%) to 0.64 ha (14%). Furthermore, we also observed that the effect of global positioning system location errors in AGB modelling would be negated by increasing plot size. Numéro de notice : A2022-587 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1864029 Date de publication en ligne : 29/12/2020 En ligne : https://doi.org/10.1080/10106049.2020.1864029 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101361 [article]

A second-order attention network for glacial lake segmentation from remotely sensed imagery / Shidong Wang in ISPRS Journal of photogrammetry and remote sensing, vol 189 (July 2022)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 189 (July 2022) . - pp 289 - 301 Titre : A second-order attention network for glacial lake segmentation from remotely sensed imagery Type de document : Article/Communication Auteurs : Shidong Wang, Auteur ; Maria V. Peppa, Auteur ; Wen Xiao, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 289 - 301 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] attention (apprentissage automatique) [Termes IGN] changement climatique [Termes IGN] covariance [Termes IGN] image Landsat-8 [Termes IGN] Inde [Termes IGN] itération [Termes IGN] lac glaciaire [Termes IGN] réflectance de surface [Termes IGN] segmentation d'image [Termes IGN] tenseur Résumé : (auteur) Climate change is increasing the risk of glacial lake outburst floods (GLOFs) in many of the world’s most vulnerable and high mountain regions. Simultaneously, remote sensing technologies now facilitate continuous monitoring of glacial lake evolution around the globe, although accurate and reliable automated glacial lake mapping from satellite data remains challenging. In this study, a Second-order Attention Network (SoAN) is devised for the automated segmentation of lakes from satellite imagery. In particular, a novel Second-order Attention Module (SoAM) is proposed to capture the long-range spatial dependencies and establish channel attention derived from the covariance representations of local features. Furthermore, as the dimensions of the input and output tensors are identical and it simply relies on matrix calculations, the proposed SoAM can be embedded into different positions of a given architecture while maintaining similar reference speed. The designed network is implemented on Landsat-8 imagery and outputs are compared against representative deep learning models, demonstrating improved results with a Dice of 81.02% and a F2 Score of 85.17%. Numéro de notice : A2022-470 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.05.007 Date de publication en ligne : 29/05/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.05.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100814 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2022071	SL	Revue	Centre de documentation	Revues en salle	Disponible

Semantic feature-constrained multitask siamese network for building change detection in high-spatial-resolution remote sensing imagery / Qian Shen in ISPRS Journal of photogrammetry and remote sensing, vol 189 (July 2022)  

Permalink

Simulation-driven 3D forest growth forecasting based on airborne topographic LiDAR data and shading / Štefan Kohek in International journal of applied Earth observation and geoinformation, vol 111 (July 2022)  

Permalink

Street-view imagery guided street furniture inventory from mobile laser scanning point clouds / Yuzhou Zhou in ISPRS Journal of photogrammetry and remote sensing, vol 189 (July 2022)  

Permalink

A dual-generator translation network fusing texture and structure features for SAR and optical image matching / Han Nie in Remote sensing, Vol 14 n° 12 (June-2 2022)  

Permalink

Encoder-decoder structure with multiscale receptive field block for unsupervised depth estimation from monocular video / Songnan Chen in Remote sensing, Vol 14 n° 12 (June-2 2022)  

Permalink

Estimating feature extraction changes of Berkelah Forest, Malaysia from multisensor remote sensing data using and object-based technique / Syaza Rozali in Geocarto international, vol 37 n° 11 ([15/06/2022])  

Permalink

How large-scale bark beetle infestations influence the protective effects of forest stands against avalanches: A case study in the Swiss Alps / Marion E. Caduff in Forest ecology and management, vol 514 (June-15 2022)  

Permalink

3D browsing of wide-angle fisheye images under view-dependent perspective correction / Mingyi Huang in Photogrammetric record, vol 37 n° 178 (June 2022)  

Permalink

3D modeling method for dome structure using digital geological map and DEM / Xian-Yu Liu in ISPRS International journal of geo-information, vol 11 n° 6 (June 2022)  

Permalink

Adversarial defenses for object detectors based on Gabor convolutional layers / Abdollah Amirkhani in The Visual Computer, vol 38 n° 6 (June 2022)  

Permalink