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Neural map style transfer exploration with GANs / Sidonie Christophe in International journal of cartography, vol 8 n° 1 (March 2022)
[article]
Titre : Neural map style transfer exploration with GANs Type de document : Article/Communication Auteurs : Sidonie Christophe , Auteur ; Samuel Mermet , Auteur ; Morgan Laurent, Auteur ; Guillaume Touya , Auteur Année de publication : 2022 Projets : 1-Pas de projet / Article en page(s) : pp 18 - 36 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] apprentissage profond
[Termes IGN] classification non dirigée
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] grille d'échantillonnage
[Termes IGN] orthoimage
[Termes IGN] représentation cartographique
[Termes IGN] réseau antagoniste génératif
[Termes IGN] style cartographique
[Termes IGN] visualisation cartographique
[Vedettes matières IGN] GéovisualisationRésumé : (auteur) Neural Style Transfer is a Computer Vision topic intending to transfer the visual appearance or the style of images to other images. Developments in deep learning nicely generate stylized images from texture-based examples or transfer the style of a photograph to another one. In map design, the style is a multi-dimensional complex problem related to recognizable visual salient features and topological arrangements, supporting the description of geographic spaces at a specific scale. The map style transfer is still at stake to generate a diversity of possible new styles to render geographical features. Generative adversarial Networks (GANs) techniques, well supporting image-to-image translation tasks, offer new perspectives for map style transfer. We propose to use accessible GAN architectures, in order to experiment and assess neural map style transfer to ortho-images, while using different map designs of various geographic spaces, from simple-styled (Plan maps) to complex-styled (old Cassini, Etat-Major, or Scan50 B&W). This transfer task and our global protocol are presented, including the sampling grid, the training and test of Pix2Pix and CycleGAN models, such as the perceptual assessment of the generated outputs. Promising results are discussed, opening research issues for neural map style transfer exploration with GANs. Numéro de notice : A2022-172 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/23729333.2022.2031554 Date de publication en ligne : 13/02/2022 En ligne : https://doi.org/10.1080/23729333.2022.2031554 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99807
in International journal of cartography > vol 8 n° 1 (March 2022) . - pp 18 - 36[article]Probabilistic unsupervised classification for large-scale analysis of spectral imaging data / Emmanuel Paradis in International journal of applied Earth observation and geoinformation, vol 107 (March 2022)
[article]
Titre : Probabilistic unsupervised classification for large-scale analysis of spectral imaging data Type de document : Article/Communication Auteurs : Emmanuel Paradis, Auteur Année de publication : 2022 Article en page(s) : n° 102675 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de groupement
[Termes IGN] analyse spectrale
[Termes IGN] classification barycentrique
[Termes IGN] classification ISODATA
[Termes IGN] classification non dirigée
[Termes IGN] classification par nuées dynamiques
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] détection de changement
[Termes IGN] entropie
[Termes IGN] image hyperspectrale
[Termes IGN] image multibande
[Termes IGN] Matlab
[Termes IGN] occupation du solRésumé : (auteur) Land cover classification of remote sensing data is a fundamental tool to study changes in the environment such as deforestation or wildfires. A current challenge is to quantify land cover changes with real-time, large-scale data from modern hyper- or multispectral sensors. A range of methods are available for this task, several of them being based on the k-means classification method which is efficient when classes of land cover are well separated. Here a new algorithm, called probabilistic k-means, is presented to solve some of the limitations of the standard k-means. It is shown that the new algorithm performs better than the standard k-means when the data are noisy. If the number of land cover classes is unknown, an entropy-based criterion can be used to select the best number of classes. The proposed new algorithm is implemented in a combination of R and C computer codes which is particularly efficient with large data sets: a whole image with more than 3 million pixels and covering more than 10,000 km2 can be analysed in a few minutes. Four applications with hyperspectral and multispectral data are presented. For the data sets with ground truth data, the overall accuracy of the probabilistic k-means was substantially improved compared to the standard k-means. One of these data sets includes more than 120 million pixels, demonstrating the scalability of the proposed approach. These developments open new perspectives for the large scale analysis of remote sensing data. All computer code are available in an open-source package called sentinel. Numéro de notice : A2022-193 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.jag.2022.102675 Date de publication en ligne : 06/01/2022 En ligne : https://doi.org/10.1016/j.jag.2022.102675 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99954
in International journal of applied Earth observation and geoinformation > vol 107 (March 2022) . - n° 102675[article]Simultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3 / Nima Pahlevan in Remote sensing of environment, vol 270 (March 2022)
[article]
Titre : Simultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3 Type de document : Article/Communication Auteurs : Nima Pahlevan, Auteur ; Brandon Smith, Auteur ; Krista Alikas, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 112860 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] appariement d'images
[Termes IGN] apprentissage automatique
[Termes IGN] chlorophylle
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] classification par Perceptron multicouche
[Termes IGN] correction atmosphérique
[Termes IGN] données multisources
[Termes IGN] eaux côtières
[Termes IGN] image Landsat-OLI
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-OLCI
[Termes IGN] matière organique
[Termes IGN] Oregon (Etats-Unis)
[Termes IGN] qualité des eauxRésumé : (auteur) Constructing multi-source satellite-derived water quality (WQ) products in inland and nearshore coastal waters from the past, present, and future missions is a long-standing challenge. Despite inherent differences in sensors’ spectral capability, spatial sampling, and radiometric performance, research efforts focused on formulating, implementing, and validating universal WQ algorithms continue to evolve. This research extends a recently developed machine-learning (ML) model, i.e., Mixture Density Networks (MDNs) (Pahlevan et al., 2020; Smith et al., 2021), to the inverse problem of simultaneously retrieving WQ indicators, including chlorophyll-a (Chla), Total Suspended Solids (TSS), and the absorption by Colored Dissolved Organic Matter at 440 nm (acdom(440)), across a wide array of aquatic ecosystems. We use a database of in situ measurements to train and optimize MDN models developed for the relevant spectral measurements (400–800 nm) of the Operational Land Imager (OLI), MultiSpectral Instrument (MSI), and Ocean and Land Color Instrument (OLCI) aboard the Landsat-8, Sentinel-2, and Sentinel-3 missions, respectively. Our two performance assessment approaches, namely hold-out and leave-one-out, suggest significant, albeit varying degrees of improvements with respect to second-best algorithms, depending on the sensor and WQ indicator (e.g., 68%, 75%, 117% improvements based on the hold-out method for Chla, TSS, and acdom(440), respectively from MSI-like spectra). Using these two assessment methods, we provide theoretical upper and lower bounds on model performance when evaluating similar and/or out-of-sample datasets. To evaluate multi-mission product consistency across broad spatial scales, map products are demonstrated for three near-concurrent OLI, MSI, and OLCI acquisitions. Overall, estimated TSS and acdom(440) from these three missions are consistent within the uncertainty of the model, but Chla maps from MSI and OLCI achieve greater accuracy than those from OLI. By applying two different atmospheric correction processors to OLI and MSI images, we also conduct matchup analyses to quantify the sensitivity of the MDN model and best-practice algorithms to uncertainties in reflectance products. Our model is less or equally sensitive to these uncertainties compared to other algorithms. Recognizing their uncertainties, MDN models can be applied as a global algorithm to enable harmonized retrievals of Chla, TSS, and acdom(440) in various aquatic ecosystems from multi-source satellite imagery. Local and/or regional ML models tuned with an apt data distribution (e.g., a subset of our dataset) should nevertheless be expected to outperform our global model. Numéro de notice : A2022-126 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112860 Date de publication en ligne : 04/01/2022 En ligne : https://doi.org/10.1016/j.rse.2021.112860 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99705
in Remote sensing of environment > vol 270 (March 2022) . - n° 112860[article]Towards low vegetation identification: A new method for tree crown segmentation from LiDAR data based on a symmetrical structure detection algorithm (SSD) / Langning Huo in Remote sensing of environment, vol 270 (March 2022)
[article]
Titre : Towards low vegetation identification: A new method for tree crown segmentation from LiDAR data based on a symmetrical structure detection algorithm (SSD) Type de document : Article/Communication Auteurs : Langning Huo, Auteur ; Eva Lindberg, Auteur ; Johan Holmgren, Auteur Année de publication : 2022 Article en page(s) : n° 112857 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] diamètre à hauteur de poitrine
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] forêt boréale
[Termes IGN] hauteur à la base du houppier
[Termes IGN] houppier
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] segmentation
[Termes IGN] semis de points
[Termes IGN] sous-bois
[Termes IGN] sous-étage
[Termes IGN] strate végétale
[Termes IGN] structure d'un peuplement forestier
[Termes IGN] SuèdeRésumé : (auteur) Obtaining low vegetation data is important in order to quantify the structural characteristics of a forest. Dense three-dimensional (3D) laser scanning data can provide information on the vertical profile of a forest. However, most studies have focused on the dominant and subdominant layers of the forest, while few studies have tried to delineate the low vegetation. To address this issue, we propose a framework for individual tree crown (ITC) segmentation from laser data that focuses on both overstory and understory trees. The framework includes 1) a new algorithm (SSD) for 3D ITC segmentation of dominant trees, by detecting the symmetrical structure of the trees, and 2) removing points of dominant trees and mean shift clustering of the low vegetation. The framework was tested on a boreal forest in Sweden and the performance was compared 1) between plots with different stem density levels, vertical complexities, and tree species composition, and 2) using airborne laser scanning (ALS) data, terrestrial laser scanning (TLS) data, and merged ALS and TLS data (ALS + TLS data). The proposed framework achieved detection rates of 0.87 (ALS + TLS), 0.86 (TLS), and 0.76 (ALS) when validated with field-inventory data (of trees with a diameter at breast height ≥ 4 cm). When validating the estimated number of understory trees by visual interpretation, the framework achieved 19%, 21%, and 39% root-mean-square error values with ALS + TLS, TLS, and ALS data, respectively. These results show that the SSD algorithm can successfully separate laser points of overstory and understory trees, ensuring the detection and segmentation of low vegetation in forest. The proposed framework can be used with both ALS and TLS data, and achieve ITC segmentation for forests with various structural attributes. The results also illustrate the potential of using ALS data to delineate low vegetation. Numéro de notice : A2022-127 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112857 Date de publication en ligne : 03/01/2022 En ligne : https://doi.org/10.1016/j.rse.2021.112857 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99707
in Remote sensing of environment > vol 270 (March 2022) . - n° 112857[article]Traffic sign three-dimensional reconstruction based on point clouds and panoramic images / Minye Wang in Photogrammetric record, vol 37 n° 177 (March 2022)
[article]
Titre : Traffic sign three-dimensional reconstruction based on point clouds and panoramic images Type de document : Article/Communication Auteurs : Minye Wang, Auteur ; Rufei Liu, Auteur ; Jiben Yang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 87 - 110 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] extraction de traits caractéristiques
[Termes IGN] image panoramique
[Termes IGN] lidar mobile
[Termes IGN] reconstruction 3D
[Termes IGN] semis de points
[Termes IGN] signalisation routièreRésumé : (auteur) Traffic signs are a very important source of information for drivers and pilotless automobiles. With the advance of Mobile LiDAR System (MLS), massive point clouds have been applied in three-dimensional digital city modelling. However, traffic signs in MLS point clouds are low density, colourless and incomplete. This paper presents a new method for the reconstruction of vertical rectangle traffic sign point clouds based on panoramic images. In this method, traffic sign point clouds are extracted based on arc feature and spatial semantic features analysis. Traffic signs in images are detected by colour and shape features and a convolutional neural network. Traffic sign point cloud and images are registered based on outline features. Finally, traffic sign points match traffic sign pixels to reconstruct the traffic sign point cloud. Experimental results have demonstrated that this proposed method can effectively obtain colourful and complete traffic sign point clouds with high resolution. Numéro de notice : A2022-254 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12398 Date de publication en ligne : 05/03/2022 En ligne : https://doi.org/10.1111/phor.12398 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100217
in Photogrammetric record > vol 37 n° 177 (March 2022) . - pp 87 - 110[article]Ultrahigh-resolution boreal forest canopy mapping: Combining UAV imagery and photogrammetric point clouds in a deep-learning-based approach / Linyuan Li in International journal of applied Earth observation and geoinformation, vol 107 (March 2022)PermalinkUnderstanding the geodetic signature of large aquifer systems: Example of the Ozark plateaus in central United States / Stacy Larochelle in Journal of geophysical research : Solid Earth, vol 127 n° 3 (March 2022)PermalinkUnderstanding the movement predictability of international travelers using a nationwide mobile phone dataset collected in South Korea / Yang Xu in Computers, Environment and Urban Systems, vol 92 (March 2022)PermalinkUsing street view images to identify road noise barriers with ensemble classification model and geospatial analysis / Kai Zhang in Sustainable Cities and Society, vol 78 (March 2022)PermalinkVisual vs internal attention mechanisms in deep neural networks for image classification and object detection / Abraham Montoya Obeso in Pattern recognition, vol 123 (March 2022)PermalinkA method of vision aided GNSS positioning using semantic information in complex urban environment / Rui Zhai in Remote sensing, vol 14 n° 4 (February-2 2022)PermalinkMulti-parameter risk mapping of Qazvin aquifer by classic and fuzzy clustering techniques / Saman Javadi in Geocarto international, vol 37 n° 4 ([15/02/2022])PermalinkMulti-species individual tree segmentation and identification based on improved mask R-CNN and UAV imagery in mixed forests / Chong Zhang in Remote sensing, vol 14 n° 4 (February-2 2022)PermalinkSimulation of future forest and land use/cover changes (2019–2039) using the cellular automata-Markov model / Hasan Aksoy in Geocarto international, vol 37 n° 4 ([15/02/2022])PermalinkA combination of convolutional and graph neural networks for regularized road surface extraction / Jingjing Yan in IEEE Transactions on geoscience and remote sensing, vol 60 n° 2 (February 2022)PermalinkDecision fusion of deep learning and shallow learning for marine oil spill detection / Junfang Yang in Remote sensing, vol 14 n° 3 (February-1 2022)PermalinkDetection of damaged buildings after an earthquake with convolutional neural networks in conjunction with image segmentation / Ramazan Unlu in The Visual Computer, vol 38 n° 2 (February 2022)PermalinkDynamic modelling of rice leaf area index with quad-source optical imagery and machine learning regression models / Lamin R. Mansaray in Geocarto international, vol 37 n° 3 ([01/02/2022])PermalinkExploring the advantages of the maximum entropy model in calibrating cellular automata for urban growth simulation: a comparative study of four methods / Bin Zhang in GIScience and remote sensing, vol 59 n° 1 (2022)PermalinkGazPNE: annotation-free deep learning for place name extraction from microblogs leveraging gazetteer and synthetic data by rules / Xuke Hu in International journal of geographical information science IJGIS, vol 36 n° 2 (February 2022)PermalinkGisGCN: a visual graph-based framework to match geographical areas through time / Margarita Khokhlova in ISPRS International journal of geo-information, vol 11 n° 2 (February 2022)PermalinkMapping abundance distributions of allergenic tree species in urbanized landscapes: A nation-wide study for Belgium using forest inventory and citizen science data / Sébastien Dujardin in Landscape and Urban Planning, vol 218 (February 2022)PermalinkMonthly mapping of forest harvesting using dense time series Sentinel-1 SAR imagery and deep learning / Feng Zhao in Remote sensing of environment, vol 269 (February 2022)PermalinkNovel model for predicting individuals’ movements in dynamic regions of interest / Xiaoqi Shen in GIScience and remote sensing, vol 59 n° 1 (2022)PermalinkPCEDNet: a lightweight neural network for fast and interactive edge detection in 3D point clouds / Chems-Eddine Himeur in ACM Transactions on Graphics, TOG, Vol 41 n° 1 (February 2022)Permalink