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Estimating generalized measures of local neighbourhood context from multispectral satellite images using a convolutional neural network / Alex David Singleton in Computers, Environment and Urban Systems, vol 95 (July 2022)  

Public [article]  inComputers, Environment and Urban Systems > vol 95 (July 2022) . - n° 101802 Titre : Estimating generalized measures of local neighbourhood context from multispectral satellite images using a convolutional neural network Type de document : Article/Communication Auteurs : Alex David Singleton, Auteur ; Dani Arribas-Bel, Auteur ; John Murray, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101802 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse de groupement [Termes IGN] analyse en composantes principales [Termes IGN] apprentissage automatique [Termes IGN] bâtiment [Termes IGN] carte d'occupation du sol [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] Grande-Bretagne [Termes IGN] image multibande [Termes IGN] image Sentinel-MSI [Termes IGN] morphologie urbaine [Termes IGN] pondération [Termes IGN] processeur graphique Résumé : (auteur) The increased availability of high-resolution multispectral imagery captured by remote sensing platforms provides new opportunities for the characterisation and differentiation of urban context. The discovery of generalized latent representations from such data are however under researched within the social sciences. As such, this paper exploits advances in machine learning to implement a new method of capturing measures of urban context from multispectral satellite imagery at a very small area level through the application of a convolutional autoencoder (CAE). The utility of outputs from the CAE is enhanced through the application of spatial weighting, and the smoothed outputs are then summarised using cluster analysis to generate a typology comprising seven groups describing salient patterns of differentiated urban context. The limits of the technique are discussed with reference to the resolution of the satellite data utilised within the study and the interaction between the geography of the input data and the learned structure. The method is implemented within the context of Great Britain, however, is applicable to any location where similar high resolution multispectral imagery are available. Numéro de notice : A2022-370 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101802 Date de publication en ligne : 19/04/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101802 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100606 [article]

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

Public [article]  inPhotogrammetric record > vol 37 n° 178 (June 2022) . - pp 185 - 207 Titre : 3D browsing of wide-angle fisheye images under view-dependent perspective correction Type de document : Article/Communication Auteurs : Mingyi Huang, Auteur ; Jun Wu, Auteur ; Zhiyong Peng, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 185 - 207 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] correction d'image [Termes IGN] distorsion d'image [Termes IGN] étalonnage d'instrument [Termes IGN] image hémisphérique [Termes IGN] objectif très grand angulaire [Termes IGN] panorama sphérique [Termes IGN] perspective [Termes IGN] processeur graphique [Termes IGN] projection orthogonale [Termes IGN] projection perspective Résumé : (auteur) This paper presents a novel technique for 3D browsing of wide-angle fisheye images using view-dependent perspective correction (VDPC). First, the fisheye imaging model with interior orientation parameters (IOPs) is established. Thereafter, a VDPC model for wide-angle fisheye images is proposed that adaptively selects correction planes for different areas of the image format. Finally, the wide-angle fisheye image is re-projected to obtain the visual effect of browsing in hemispherical space, using the VDPC model and IOPs of the fisheye camera calibrated using the ideal projection ellipse constraint. The proposed technique is tested on several downloaded internet images with unknown IOPs. Results show that the proposed VDPC model achieves a more uniform perspective correction of fisheye images in different areas, and preserves the detailed information with greater flexibility compared with the traditional perspective projection conversion (PPC) technique. The proposed algorithm generates a corrected image of 512 × 512 pixels resolution at a speed of 58 fps when run on a pure central processing unit (CPU) processor. With an ordinary graphics processing unit (GPU) processor, a corrected image of 1024 × 1024 pixels resolution can be generated at 60 fps. Therefore, smooth 3D visualisation of a fisheye image can be realised on a computer using the proposed algorithm, which may benefit applications such as panorama surveillance, robot navigation, etc. Numéro de notice : A2022-518 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12410 Date de publication en ligne : 10/05/2022 En ligne : https://doi.org/10.1111/phor.12410 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101068 [article]

Detecting and visualizing observation hot-spots in massive volunteer-contributed geographic data across spatial scales using GPU-accelerated kernel density estimation / Guiming Zhang in ISPRS International journal of geo-information, vol 11 n° 1 (January 2022)  

Public [article]  inISPRS International journal of geo-information > vol 11 n° 1 (January 2022) . - n° 55 Titre : Detecting and visualizing observation hot-spots in massive volunteer-contributed geographic data across spatial scales using GPU-accelerated kernel density estimation Type de document : Article/Communication Auteurs : Guiming Zhang, Auteur Année de publication : 2022 Article en page(s) : n° 55 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] données localisées des bénévoles [Termes IGN] données massives [Termes IGN] estimation par noyau [Termes IGN] exploration de données géographiques [Termes IGN] géovisualisation [Termes IGN] processeur graphique [Termes IGN] qualité des données [Termes IGN] réseau social [Termes IGN] tâche claire Résumé : (auteur) Volunteer-contributed geographic data (VGI) is an important source of geospatial big data that support research and applications. A major concern on VGI data quality is that the underlying observation processes are inherently biased. Detecting observation hot-spots thus helps better understand the bias. Enabled by the parallel kernel density estimation (KDE) computational tool that can run on multiple GPUs (graphics processing units), this study conducted point pattern analyses on tens of millions of iNaturalist observations to detect and visualize volunteers’ observation hot-spots across spatial scales. It was achieved by setting varying KDE bandwidths in accordance with the spatial scales at which hot-spots are to be detected. The succession of estimated density surfaces were then rendered at a sequence of map scales for visual detection of hot-spots. This study offers an effective geovisualization scheme for hierarchically detecting hot-spots in massive VGI datasets, which is useful for understanding the pattern-shaping drivers that operate at multiple spatial scales. This research exemplifies a computational tool that is supported by high-performance computing and capable of efficiently detecting and visualizing multi-scale hot-spots in geospatial big data and contributes to expanding the toolbox for geospatial big data analytics. Numéro de notice : A2022-091 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11010055 Date de publication en ligne : 12/01/2022 En ligne : https://doi.org/10.3390/ijgi11010055 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99507 [article]

Recursive Gauss-Helmert model with equality constraints applied to the efficient system calibration of a 3D laser scanner / Sören Vogel in Journal of applied geodesy, vol 16 n° 1 (January 2022)  

Public [article]  inJournal of applied geodesy > vol 16 n° 1 (January 2022) . - pp 37 - 57 Titre : Recursive Gauss-Helmert model with equality constraints applied to the efficient system calibration of a 3D laser scanner Type de document : Article/Communication Auteurs : Sören Vogel, Auteur ; Dominik Ernst, Auteur ; Ingo Neumann, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 37 - 57 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s) [Termes IGN] contrainte d'intégrité [Termes IGN] étalonnage d'instrument [Termes IGN] filtre de Kalman [Termes IGN] géoréférencement [Termes IGN] modèle de Gauss-Helmert [Termes IGN] positionnement cinématique [Termes IGN] processeur graphique [Termes IGN] télémètre laser aéroporté Résumé : (auteur) Sensors for environmental perception are nowadays applied in numerous vehicles and are expected to be used in even higher quantities for future autonomous driving. This leads to an increasing amount of observation data that must be processed reliably and accurately very quickly. For this purpose, recursive approaches are particularly suitable in terms of their efficiency when powerful CPUs and GPUs are uneconomical, too large, or too heavy for certain applications. If explicit functional relationships between the available observations and the requested parameters are used to process and adjust the observation data, complementary approaches exist. The situation is different for implicit relationships, which could not be considered recursively for a long time but only in the context of batch adjustments. In this contribution, a recursive Gauss-Helmert model is presented that can handle explicit and implicit equations and thus allows high flexibility. This recursive estimator is based on a Kalman filter for implicit measurement equations, which has already been used for georeferencing kinematic multi-sensor systems (MSS) in urban environments. Furthermore, different methods for introducing additional information using constraints and the resulting added value are shown. Practical application of the methodology is given by an example for the calibration of a laser scanner for a MSS. Numéro de notice : A2022-053 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1515/jag-2021-0026 Date de publication en ligne : 15/10/2021 En ligne : https://doi.org/10.1515/jag-2021-0026 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99448 [article]

Efficient image dataset classification difficulty estimation for predicting deep-learning accuracy / Florian Scheidegger in The Visual Computer, vol 37 n° 6 (June 2021)  

Public [article]  inThe Visual Computer > vol 37 n° 6 (June 2021) . - pp 1593 - 1610 Titre : Efficient image dataset classification difficulty estimation for predicting deep-learning accuracy Type de document : Article/Communication Auteurs : Florian Scheidegger, Auteur ; Roxana Istrate, Auteur ; Giovanni Mariani, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1593 - 1610 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] architecture de réseau [Termes IGN] classification par nuées dynamiques [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] distance de Fréchet [Termes IGN] extraction de traits caractéristiques [Termes IGN] jeu de données [Termes IGN] précision de la classification [Termes IGN] processeur graphique Résumé : (auteur) In the deep-learning community, new algorithms are published at a very fast pace. Therefore, solving an image classification problem for new datasets becomes a challenging task, as it requires to re-evaluate published algorithms and their different configurations in order to find a close to optimal classifier. To facilitate this process, before biasing our decision toward a class of neural networks or running an expensive search over the network space, we propose to estimate the classification difficulty of the dataset. Our method computes a single number that characterizes the dataset difficulty 97× faster than training state-of-the-art networks. The proposed method can be used in combination with network topology and hyper-parameter search optimizers to efficiently drive the search toward promising neural network configurations. Numéro de notice : A2021-533 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00371-020-01922-5 Date de publication en ligne : 28/07/2020 En ligne : https://doi.org/10.1007/s00371-020-01922-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97993 [article]

A Bayesian displacement field approach to accurate registration of SAR images / Mingtao Ding in Geocarto international, vol 36 n° 9 ([15/05/2021])  

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Hyperspectral image denoising via clustering-based latent variable in variational Bayesian framework / Peyman Azimpour in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)  

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Developing graphics frameworks with Python and OpenGL / Lee Stemkoski (2021)  

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Rasterisation-based progressive photon mapping / Iordanis Evangelou in The Visual Computer, vol 36 n° 10 - 12 (October 2020)  

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CSVM architectures for pixel-wise object detection in high-resolution remote sensing images / Youyou Li in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)  

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Local terrain modification method considering physical feature constraints for vector elements / Jiangfeng She in Cartography and Geographic Information Science, Vol 47 n° 5 (September 2020)  

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Image processing applications in object detection and graph matching: from Matlab development to GPU framework / Beibei Cui (2020)  

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Bayesian iterative reconstruction methods for 3D X-ray Computed Tomography / Camille Chapdelaine (2019)  

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Machine learning - advanced techniques and emerging applications / Hamed Farhadi (2018)  

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An efficient data organization and scheduling strategy for accelerating large vector data rendering / Mingqiang Guo in Transactions in GIS, vol 21 n° 6 (December 2017)  

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