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Emotional habitat: mapping the global geographic distribution of human emotion with physical environmental factors using a species distribution model / Yizhuo Li in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)
[article]
Titre : Emotional habitat: mapping the global geographic distribution of human emotion with physical environmental factors using a species distribution model Type de document : Article/Communication Auteurs : Yizhuo Li, Auteur ; Teng Fei, Auteur ; Yingjing Huang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 227 - 249 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique
[Termes IGN] comportement
[Termes IGN] détection de visage
[Termes IGN] distribution spatiale
[Termes IGN] données environnementales
[Termes IGN] émotion
[Termes IGN] entropie
[Termes IGN] psychologie
[Termes IGN] reconnaissance faciale
[Termes IGN] sciences humaines
[Termes IGN] visionRésumé : (auteur) Human emotion is an intrinsic psychological state that is influenced by human thoughts and behaviours. Human emotion distribution has been regarded as an important part of emotional geography research. However, it is difficult to form a global scaled map reflecting human emotions at the same sampling density because various emotional sampling data are usually positive occurrences without absence data. In this study, a methodological framework for mapping the global geographic distribution of human emotion is proposed and applied, combining a species distribution model with physical environment factors. State-of-the-art affective computing technology is used to extract human emotions from facial expressions in Flickr photos. Various human emotions are considered as different species to form their ‘habitats’ and predict the suitability, termed as ‘Emotional Habitat’. To our knowledge, this framework is the first method to predict emotional distribution from an ecological perspective. Different geographic distributions of seven dimensional emotions are explored and depicted, and emotional diversity and abnormality are detected at the global scale. These results confirm the effectiveness of our framework and offer new insights to understand the relationship between human emotions and the physical environment. Moreover, our method facilitates further rigorous exploration in emotional geography and enriches its content. Numéro de notice : A2021-037 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1755040 Date de publication en ligne : 24/04/2020 En ligne : https://doi.org/10.1080/13658816.2020.1755040 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96746
in International journal of geographical information science IJGIS > vol 35 n° 2 (February 2021) . - pp 227 - 249[article]Nonlocal graph convolutional networks for hyperspectral image classification / Lichao Mou in IEEE Transactions on geoscience and remote sensing, Vol 58 n° 12 (December 2020)
[article]
Titre : Nonlocal graph convolutional networks for hyperspectral image classification Type de document : Article/Communication Auteurs : Lichao Mou, Auteur ; Xiaoqiang Lu, Auteur ; Xuelong Li, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 8246 - 8257 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] classification semi-dirigée
[Termes IGN] entropie
[Termes IGN] graphe
[Termes IGN] image hyperspectrale
[Termes IGN] réseau neuronal récurrentRésumé : (auteur) Over the past few years making use of deep networks, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), classifying hyperspectral images has progressed significantly and gained increasing attention. In spite of being successful, these networks need an adequate supply of labeled training instances for supervised learning, which, however, is quite costly to collect. On the other hand, unlabeled data can be accessed in almost arbitrary amounts. Hence it would be conceptually of great interest to explore networks that are able to exploit labeled and unlabeled data simultaneously for hyperspectral image classification. In this article, we propose a novel graph-based semisupervised network called nonlocal graph convolutional network (nonlocal GCN). Unlike existing CNNs and RNNs that receive pixels or patches of a hyperspectral image as inputs, this network takes the whole image (including both labeled and unlabeled data) in. More specifically, a nonlocal graph is first calculated. Given this graph representation, a couple of graph convolutional layers are used to extract features. Finally, the semisupervised learning of the network is done by using a cross-entropy error over all labeled instances. Note that the nonlocal GCN is end-to-end trainable. We demonstrate in extensive experiments that compared with state-of-the-art spectral classifiers and spectral–spatial classification networks, the nonlocal GCN is able to offer competitive results and high-quality classification maps (with fine boundaries and without noisy scattered points of misclassification). Numéro de notice : A2020-739 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2973363 Date de publication en ligne : 12/05/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2973363 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96365
in IEEE Transactions on geoscience and remote sensing > Vol 58 n° 12 (December 2020) . - pp 8246 - 8257[article]Active and incremental learning for semantic ALS point cloud segmentation / Yaping Lin in ISPRS Journal of photogrammetry and remote sensing, vol 169 (November 2020)
[article]
Titre : Active and incremental learning for semantic ALS point cloud segmentation Type de document : Article/Communication Auteurs : Yaping Lin, Auteur ; M. George Vosselman, Auteur ; Yanpeng Cao, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 73 - 92 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage profond
[Termes IGN] classification dirigée
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] entropie
[Termes IGN] incertitude des données
[Termes IGN] itération
[Termes IGN] segmentation sémantique
[Termes IGN] semis de pointsRésumé : (auteur) Supervised training of a deep neural network for semantic segmentation of point clouds requires a large amount of labelled data. Nowadays, it is easy to acquire a huge number of points with high density in large-scale areas using current LiDAR and photogrammetric techniques. However it is extremely time-consuming to manually label point clouds for model training. In this paper, we propose an active and incremental learning strategy to iteratively query informative point cloud data for manual annotation and the model is continuously trained to adapt to the newly labelled samples in each iteration. We evaluate the data informativeness step by step and effectively and incrementally enrich the model knowledge. The data informativeness is estimated by two data dependent uncertainty metrics (point entropy and segment entropy) and one model dependent metric (mutual information). The proposed methods are tested on two datasets. The results indicate the proposed uncertainty metrics can enrich current model knowledge by selecting informative samples, such as considering points with difficult class labels and choosing target objects with various geometries in the labelled training pool. Compared to random selection, our metrics provide valuable information to significantly reduce the labelled training samples. In contrast with training from scratch, the incremental fine-tuning strategy significantly save the training time. Numéro de notice : A2020-638 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.09.003 Date de publication en ligne : 14/09/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.09.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96061
in ISPRS Journal of photogrammetry and remote sensing > vol 169 (November 2020) . - pp 73 - 92[article]Réservation
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[article]
Titre : Variable DEM generalization using local entropy for terrain representation through scale Type de document : Article/Communication Auteurs : Paulo Raposo, Auteur Année de publication : 2020 Article en page(s) : pp 99 - 120 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] courbe de niveau
[Termes IGN] entropie
[Termes IGN] filtre passe-bas
[Termes IGN] généralisation cartographique
[Termes IGN] lissage de valeur
[Termes IGN] modèle numérique de surface
[Termes IGN] représentation multiple
[Termes IGN] voisinage (relation topologique)
[Vedettes matières IGN] GénéralisationRésumé : (auteur) An automated method of variable digital elevation model (DEM) smoothing is presented. Using variably sized kernels to perform filtering, the method is driven by the entropy of local z-values in the DEM, i.e. the amount of information necessary to convey the elevation variety in the neighborhood of each pixel. This paper presents the method in service of low-pass filtering in order to smooth the raster, though other neighborhood-based filters could be implemented as well. When used in smoothing, the method successfully retains detail in areas of higher relief variation and suppresses it in areas of lower variation, thereby retaining more salient features like ridges, peaks, or incised valleys, while diminishing flatter ones. Varying the neighborhood size with which entropy calculations are made allows for filtering through continuous map scale, enabling multi-scale representation. The method also includes a simple correction for smoothed pixels such that their z-value range reflects that of the input DEM, thereby ensuring that subsequent products such as generated contour lines remain within correct ranges. Several illustrations are given of the method's results. Numéro de notice : A2020-072 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/23729333.2019.1687973 Date de publication en ligne : 16/12/2019 En ligne : https://doi.org/10.1080/23729333.2019.1687973 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94636
in International journal of cartography > Vol 6 n° 1 (March 2020) . - pp 99 - 120[article]Image processing applications in object detection and graph matching: from Matlab development to GPU framework / Beibei Cui (2020)
Titre : Image processing applications in object detection and graph matching: from Matlab development to GPU framework Type de document : Thèse/HDR Auteurs : Beibei Cui, Auteur ; Jean-Charles Créput, Directeur de thèse Editeur : Dijon : Université Bourgogne Franche-Comté UBFC Année de publication : 2020 Importance : 137 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de Doctorat de l'Université Bourgogne Franche-Comté préparée à l'Université de Technologie de Belfort-Montbéliard, InformatiqueLangues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] appariement de graphes
[Termes IGN] détection d'objet
[Termes IGN] entropie
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] graphe planaire
[Termes IGN] Matlab
[Termes IGN] ondelette
[Termes IGN] processeur graphique
[Termes IGN] Ransac (algorithme)
[Termes IGN] reconnaissance de formesIndex. décimale : THESE Thèses et HDR Résumé : (auteur) Automatically finding correspondences between object features in images is of main interest for several applications, as object detection and tracking, flow velocity estimation, identification, registration, and many derived tasks. In this thesis, we address feature correspondence within the general framework of graph matching optimization and with the principal aim to contribute, at a final step, to the design of new and parallel algorithms and their implementation on GPU (Graphics Processing Unit) systems. Graph matching problems can have many declinations, depending on the assumptions of the application at hand. We observed a gap between applications based on local cost objective functions, and those applications with higher-order cost functions, that evaluate similarity between edges of the graphs, or hyperedges when considering hypergraphs. The former class provides convolution-based algorithms already having parallel GPU implementations. Whereas, the latter class puts the emphasis on geometric inter-feature relationships, transforming the correspondence problem to a purely geometric problem stated in a high dimensional space, generally modeled as an integer quadratic programming, for which we did not find GPU implementations available yet.Two complementary approaches were adopted in order to contribute to addressing higher-order geometric graph matching on GPU. Firstly, we study different declinations of feature correspondence problems by the use of the Matlab platform, in order to reuse and provide state-of-the-art solution methods, as well as experimental protocols and input data necessary for a GPU platform with evaluation and comparison tools against existing sequential algorithms, most of the time developed in Matlab framework. Then, the first part of this work concerns three contributions, respectively, to background and frame difference application, to feature extraction problem from images for local correspondences, and to the general graph matching problem, all based on the combination of methods derived from Matlab environment. Secondly, and based on the results of Matlab developments, we propose a new GPU framework written in CUDA C++ specifically dedicated to geometric graph matching but providing new parallel algorithms, with lower computational complexity, as the self-organizing map in the plane, derived parallel clustering algorithms, and distributed local search method. These parallel algorithms are then evaluated and compared to the state-of-the-art methods available for graph matching and following the same experimental protocol. This GPU platform constitutes our final and main proposal to contribute to bridging the gap between GPU development and higher-order graph matching. Note de contenu : 1- Introduction
2- Background
3- Background subtraction and frame difference for multi-object detection
4- Using Marr-wavelets and entropy/response to automatic feature detection
5- Affinity-preserving fixed point APRIP in Matlab framework for graph matching
6- Planar graph matching in GPU
7- Conclusion and future workNuméro de notice : 28328 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Informatique : UBFC : 2020 Organisme de stage : CIAD Dijon DOI : sans En ligne : https://tel.archives-ouvertes.fr/tel-02902973/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98402 PermalinkBurn severity analysis in Mediterranean forests using maximum entropy model trained with EO-1 Hyperion and LiDAR data / Alfonso Fernández-Manso in ISPRS Journal of photogrammetry and remote sensing, vol 155 (September 2019)PermalinkCyclic asymptotic behaviour of a population reproducing by fission into two equal parts / Etienne Bernard in Kinetic & Related Models, vol 12 n° 3 (June 2019)PermalinkVehicle detection in aerial images / Michael Ying Yang in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 4 (avril 2019)PermalinkPermalinkA simple line clustering method for spatial analysis with origin-destination data and its application to bike-sharing movement data / Biao He in ISPRS International journal of geo-information, vol 7 n° 6 (June 2018)PermalinkLocal curvature entropy-based 3D terrain representation using a comprehensive Quadtree / Giyu Chen in ISPRS Journal of photogrammetry and remote sensing, vol 139 (May 2018)PermalinkAnalyse de l'incertitude et de la précision thématique de classifications GEOBIA d'une image WorldView-2 / François Messner in Revue Française de Photogrammétrie et de Télédétection, n° 216 (février 2018)PermalinkPermalinkComparing image-based methods for assessing visual clutter in generalized maps / Guillaume Touya in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol II-3 W5 (October 2015)Permalink