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Discriminative information restoration and extraction for weakly supervised low-resolution fine-grained image recognition / Tiantian Yan in Pattern recognition, vol 127 (July 2022)  

Public [article]  inPattern recognition > vol 127 (July 2022) . - n° 108629 Titre : Discriminative information restoration and extraction for weakly supervised low-resolution fine-grained image recognition Type de document : Article/Communication Auteurs : Tiantian Yan, Auteur ; Jian Shi, Auteur ; Haojie Li, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 108629 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse discriminante [Termes IGN] arbre aléatoire minimum [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] extraction de données [Termes IGN] granularité d'image [Termes IGN] image à basse résolution [Termes IGN] image à haute résolution [Termes IGN] relation sémantique [Termes IGN] texture d'image Résumé : (auteur) The existing methods of fine-grained image recognition mainly devote to learning subtle yet discriminative features from the high-resolution input. However, their performance deteriorates significantly when they are used for low quality images because a lot of discriminative details of images are missing. We propose a discriminative information restoration and extraction network, termed as DRE-Net, to address the problem of low-resolution fine-grained image recognition, which has widespread application potential, such as shelf auditing and surveillance scenarios. DRE-Net is the first framework for weakly supervised low-resolution fine-grained image recognition and consists of two sub-networks: (1) fine-grained discriminative information restoration sub-network (FDR) and (2) recognition sub-network with the semantic relation distillation loss (SRD-loss). The first module utilizes the structural characteristic of minimum spanning tree (MST) to establish context information for each pixel by employing the spatial structures between each pixel and other pixels, which can help FDR focus on and restore the critical texture details. The second module employs the SRD-loss to calibrate recognition sub-network by transferring the correct relationships between every two pixels on the feature map. Meanwhile the SRD-loss can further prompt the FDR to recover reliable and accurate fine-grained details and guide the recognition sub-network to perceive the discriminative features from the correct relationships. Extensive experiments on three benchmark datasets and one retail product dataset demonstrate the effectiveness of our proposed framework. Numéro de notice : A2022-555 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.patcog.2022.108629 Date de publication en ligne : 06/03/2022 En ligne : https://doi.org/10.1016/j.patcog.2022.108629 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101168 [article]

A framework for urban land use classification by integrating the spatial context of points of interest and graph convolutional neural network method / Yongyang Xu in Computers, Environment and Urban Systems, vol 95 (July 2022)  

Public [article]  inComputers, Environment and Urban Systems > vol 95 (July 2022) . - n° 101807 Titre : A framework for urban land use classification by integrating the spatial context of points of interest and graph convolutional neural network method Type de document : Article/Communication Auteurs : Yongyang Xu, Auteur ; Bo Zhou, Auteur ; Shuai Jin, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101807 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] apprentissage profond [Termes IGN] arbre aléatoire minimum [Termes IGN] distribution spatiale [Termes IGN] noeud [Termes IGN] Pékin (Chine) [Termes IGN] planification urbaine [Termes IGN] point d'intérêt [Termes IGN] réseau neuronal de graphes [Termes IGN] taxinomie [Termes IGN] trafic routier [Termes IGN] triangulation de Delaunay [Termes IGN] utilisation du sol [Termes IGN] zone urbaine Résumé : (auteur) Land-use classification plays an important role in urban planning and resource allocation and had contributed to a wide range of urban studies and investigations. With the development of crowdsourcing technology and map services, points of interest (POIs) have been widely used for recognizing urban land-use types. However, current research methods for land-use classifications have been limited to extracting the spatial relationship of POIs in research units. To close this gap, this study uses a graph-based data structure to describe the POIs in research units, with graph convolutional networks (GCNs) being introduced to extract the spatial context and urban land-use classification. First, urban scenes are built by considering the spatial context of POIs. Second, a graph structure is used to express the scenes, where POIs are treated as graph nodes. The spatial distribution relationship of POIs is considered to be the graph's edges. Third, a GCN model is designed to extract the spatial context of the scene by aggregating the information of adjacent nodes within the graph and urban land-use classification. Thus, the land-use classification can be treated as a classification on a graphic level through deep learning. Moreover, the POI spatial context can be effectively extracted during classification. Experimental results and comparative experiments confirm the effectiveness of the proposed method. Numéro de notice : A2022-460 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101807 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101807 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100622 [article]

Contributions to graph-based hierarchical analysis for images and 3D point clouds / Leonardo Gigli (2021)  

Public Titre : Contributions to graph-based hierarchical analysis for images and 3D point clouds Type de document : Thèse/HDR Auteurs : Leonardo Gigli, Auteur ; Beatriz Marcotegui, Directeur de thèse Editeur : Paris : Université Paris Sciences et Lettres Année de publication : 2021 Importance : 177 p. Format : 21 x 30 cm Note générale : bibliographie Thèse de Doctorat de l'Université PSL, Spécialité : Morphologie Mathématique Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] analyse de groupement [Termes IGN] apprentissage automatique [Termes IGN] arbre aléatoire minimum [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] extraction de traits caractéristiques [Termes IGN] extraction du réseau routier [Termes IGN] morphologie mathématique [Termes IGN] processus de hiérarchisation analytique [Termes IGN] réseau neuronal de graphes [Termes IGN] segmentation d'image [Termes IGN] semis de points [Termes IGN] texture d'image [Termes IGN] théorie des graphes Index. décimale : THESE Thèses et HDR Résumé : (auteur) Graphs are powerful mathematical structures representing a set of objects and the underlying links between pairs of objects somehow related. They are becoming increasingly popular in data science in general and in particular in image or 3D point cloud analysis. Among the wide spectra of applications, they are involved in most of the hierarchical approaches.Hierarchies are particularly important because they allow us to efficiently organize the information required and to analyze the problems at different levels of detail. In this thesis, we address the following topics. Many morphological hierarchical approaches rely on the Minimum Spanning Tree (MST). We propose an algorithm for MST computation in streaming based on a graph decomposition strategy. Thanks to this decomposition, larger images can be processed or can benefit from partial reliable information while the whole image is not completely available.Recent LiDAR developments are able to acquire large-scale and precise 3D point clouds. Many applications, such as infrastructure monitoring, urban planning, autonomous driving, precision forestry, environmental assessment, archaeological discoveries, to cite a few, are under development nowadays. We introduce a ground detection algorithm and compare it with the state of the art. The impact of reducing the point cloud density with low-cost scanners is studied, in the context of an autonomous driving application. Finally, in many hierarchical methods similarities between points are given as input. However, the metric used to compute similarities influences the quality of the final results. We exploit metric learning as a complementary tool that helps to improve the quality of hierarchies. We demonstrate the capabilities of these methods in two contexts. The first one,a texture classification of 3D surfaces. Our approach ranked second in a task organized by SHREC’20 international challenge. The second one learning the similarity function together with the optimal hierarchical clustering, in a continuous feature-based hierarchical clustering formulation. Note de contenu : Introduction 1- Graph theory and clustering 2- Point clouds 3- Ground and road detection 4- Minimum spanning tree for data streams 5- Metric learning 6- Towards Morphological Convolutions on Graphs Conclusions Numéro de notice : 28623 Affiliation des auteurs : non IGN Thématique : IMAGERIE/MATHEMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Morphologie Mathématique : Paris Sciences et Lettres : 2021 Organisme de stage : Centre de Morphologie Mathématique DOI : sans En ligne : https://pastel.hal.science/tel-03512298/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99543

Semi-supervised PolSAR image classification based on improved tri-training with a minimum spanning tree / Shuang Wang in IEEE Transactions on geoscience and remote sensing, Vol 58 n° 12 (December 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > Vol 58 n° 12 (December 2020) . - pp 8583 - 8597 Titre : Semi-supervised PolSAR image classification based on improved tri-training with a minimum spanning tree Type de document : Article/Communication Auteurs : Shuang Wang, Auteur ; Yanhe Guo, Auteur ; Wenqiang Hua, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 8583 - 8597 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] arbre aléatoire minimum [Termes IGN] classification semi-dirigée [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] image radar moirée [Termes IGN] polarimétrie radar [Termes IGN] segmentation sémantique [Termes IGN] voisinage (relation topologique) Résumé : (auteur) In this article, the terrain classifications of polarimetric synthetic aperture radar (PolSAR) images are studied. A novel semi-supervised method based on improved Tri-training combined with a neighborhood minimum spanning tree (NMST) is proposed. Several strategies are included in the method: 1) a high-dimensional vector of polarimetric features that are obtained from the coherency matrix and diverse target decompositions is constructed; 2) this vector is divided into three subvectors and each subvector consists of one-third of the polarimetric features, randomly selected. The three subvectors are used to separately train the three different base classifiers in the Tri-training algorithm to increase the diversity of classification; and 3) a help-training sample selection with the improved NMST that uses both the coherency matrix and the spatial information is adopted to select highly reliable unlabeled samples to increase the training sets. Thus, the proposed method can effectively take advantage of unlabeled samples to improve the classification. Experimental results show that with a small number of labeled samples, the proposed method achieves a much better performance than existing classification methods. Numéro de notice : A2020-743 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2988982 Date de publication en ligne : 14/05/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2988982 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96374 [article]

A novel spectral–spatial based adaptive minimum spanning forest for hyperspectral image classification / Jing Lv in Geoinformatica, vol 24 n° 4 (October 2020)  

Public [article]  inGeoinformatica > vol 24 n° 4 (October 2020) . - pp 827 - 848 Titre : A novel spectral–spatial based adaptive minimum spanning forest for hyperspectral image classification Type de document : Article/Communication Auteurs : Jing Lv, Auteur ; Huimin Zhang, Auteur ; Ming Yang, Auteur ; Wanqi Yang, Auteur Année de publication : 2020 Article en page(s) : pp 827 - 848 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] arbre aléatoire minimum [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] classification pixellaire [Termes IGN] image hyperspectrale [Termes IGN] segmentation d'image Résumé : (Auteur) The classification methods based on minimum spanning forest (MSF) have yielded impressive results for hyperspectral image. However, previous methods exist several drawbacks, i.e., marker selection methods are easily affected by boundary noise pixels, dissimilarity measure methods between pixels are inaccurate, and also image segmentation process is not robust, since they have not effectively utilized spatial information. To this end, in this paper, novel gradient-based marker selection technique, dissimilarity measures, and adaptive connection weighting method are proposed by making full use of spatial information in hyperspectral image. Concretely, for a given hyperspectral image, a pixel-wise classification is firstly performed, and meanwhile the gradient map is generated by a morphology-based algorithm. Secondly, the most reliable pixels are selected as the markers from the classification map, and then the boundary noise pixels are excluded from the marker map by using the gradient map. Thirdly, several new dissimilarity measures are proposed by incorporating gradient information or probability information of pixels. Furthermore, in the growth procedure of MSF, the connection weighting between pixels is adjusted adaptively to improve the robustness of the MSF algorithm. Finally, when building the final classification map by using the majority voting rule, the labels of the training samples are used to dominate the label prediction. Experimental results are performed on two hyperspectral image sets Indian Pines and University of Pavia with different resolutions and contexts. The proposed approach yields higher classification accuracies compared to previously proposed classification methods, and provides accurate segmentation maps. Numéro de notice : A2020-496 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10707-020-00403-0 Date de publication en ligne : 11/05/2020 En ligne : https://doi.org/10.1007/s10707-020-00403-0 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96117 [article]

Chloroplast haplotypes of Northern red oak (Quercus rubra L.) stands in Germany suggest their origin from Northeastern Canada / Jeremias Götz in Forests, vol 11 n° 9 (September 2020)  

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Using OpenStreetMap data and machine learning to generate socio-economic indicators / Daniel Feldmeyer in ISPRS International journal of geo-information, vol 9 n° 9 (September 2020)  

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Structure from motion for complex image sets / Mario Michelini in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)  

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Planar maps, random walks and circle packing / Asaf Nachmias (2020)  

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Evaluating SAR-optical sensor fusion for aboveground biomass estimation in a Brazilian tropical forest / Aline Bernarda Debastiani in Annals of forest research, vol 62 n° 1 (January - June 2019)  

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On the spatial distribution of buildings for map generalization / Zhiwei Wei in Cartography and Geographic Information Science, Vol 45 n° 6 (November 2018)  

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Automated extraction of 3D vector topographic feature line from terrain point cloud / Wei Zhou in Geocarto international, vol 33 n° 10 (October 2018)  

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A deep neural network with spatial pooling (DNNSP) for 3-D point cloud classification / Zhen Wang in IEEE Transactions on geoscience and remote sensing, vol 56 n° 8 (August 2018)  

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Evaluation of 10-year temporal and spatial variability in structure and growth across contrasting commercial thinning treatments in spruce-fir forests of northern Maine, USA / Christian Kuehne in Annals of Forest Science, vol 75 n° 1 (March 2018)  

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Efficient structure from motion for oblique UAV images based on maximal spanning tree expansion / San Jiang in ISPRS Journal of photogrammetry and remote sensing, vol 132 (October 2017)  

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