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Graph learning based on signal smoothness representation for homogeneous and heterogeneous change detection / David Alejandro Jimenez-Sierra in IEEE Transactions on geoscience and remote sensing, vol 60 n° 4 (April 2022)
[article]
Titre : Graph learning based on signal smoothness representation for homogeneous and heterogeneous change detection Type de document : Article/Communication Auteurs : David Alejandro Jimenez-Sierra, Auteur ; David Alfredo Quintero-Olaya, Auteur ; Juan Carlos Alvear-Muñoz, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 4410416 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] apprentissage non-dirigé
[Termes IGN] apprentissage profond
[Termes IGN] détection de changement
[Termes IGN] graphe
[Termes IGN] image multibande
[Termes IGN] image radar moirée
[Termes IGN] Kappa de Cohen
[Termes IGN] lissage de données
[Termes IGN] processus gaussien
[Termes IGN] réseau sémantique
[Termes IGN] segmentation d'image
[Termes IGN] seuillage
[Termes IGN] superpixelRésumé : (auteur) Graph-based methods are promising approaches for traditional and modern techniques in change detection (CD) applications. Nonetheless, some graph-based approaches omit the existence of useful priors that account for the structure of a scene, and the inter- and intra-relationships between the pixels are analyzed. To address this issue, in this article, we propose a framework for CD based on graph fusion and driven by graph signal smoothness representation. In addition to modifying the graph learning stage, in the proposed model, we apply a Gaussian mixture model for superpixel segmentation (GMMSP) as a downsampling module to reduce the computational cost required to learn the graph of the entire images. We carry out tests on 14 real cases of natural disasters, farming, and construction. The dataset contains homogeneous cases with multispectral (MS) and synthetic aperture radar (SAR) images, along with heterogeneous cases that include MS/SAR images. We compare our approach against probabilistic thresholding, unsupervised learning, deep learning, and graph-based methods. In terms of Cohen’s kappa coefficient, our proposed model based on graph signal smoothness representation outperformed state-of-the-art approaches in ten out of 14 datasets. Numéro de notice : A2022-379 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2022.3168126 Date de publication en ligne : 18/04/2022 En ligne : https://doi.org/10.1109/TGRS.2022.3168126 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100643
in IEEE Transactions on geoscience and remote sensing > vol 60 n° 4 (April 2022) . - n° 4410416[article]Graph neural network based model for multi-behavior session-based recommendation / Bo Yu in Geoinformatica, vol 26 n° 2 (April 2022)
[article]
Titre : Graph neural network based model for multi-behavior session-based recommendation Type de document : Article/Communication Auteurs : Bo Yu, Auteur ; Ruoqian Zhang, Auteur ; Wei Chen, Auteur ; Junhua Fang, Auteur Année de publication : 2022 Article en page(s) : pp 429 - 447 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] comportement
[Termes IGN] consommation
[Termes IGN] modèle de simulation
[Termes IGN] réseau neuronal de graphes
[Termes IGN] réseau sémantique
[Termes IGN] service fondé sur la positionMots-clés libres : session Résumé : (auteur) Multi-behavior session-based recommendation aims to predict the next item, such as a location-based service (LBS) or a product, to be interacted by a specific behavior type (e.g., buy or click) in a session involving multiple types of behaviors. State-of-the-art methods generally model multi-behavior dependencies in item-level, but ignore the potential of discovering useful patterns of multi-behavior transition through feature-level representation learning. Besides, sequential and non-sequential patterns should be properly fused in session modeling to capture dynamic interests within the session. To this end, this paper proposes a Graph Neural Network based Hybrid Model GNNH, which enables feature-level deeper representations of multi-behavior interaction sequences for session-based recommendation. Specifically, we first construct multi-relational item graph (MRIG) and feature graph (MRFG) based on session sequences. On top of the MRIG and MRFG, our model takes advantage of GNN to capture item and feature representations, such that global item-to-item and feature-to-feature relations are fully preserved. Afterwards, each multi-behavior session is modeled by a seamless fusion of interacted item and feature representations, where self-attention and mean-pooling are used to obtain sequential and non-sequential patterns simultaneously. Experiments on two real datasets show that the GNNH model significantly outperforms the state-of-the-art methods. Numéro de notice : A2022-326 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1007/s10707-021-00439-w Date de publication en ligne : 29/05/2021 En ligne : https://doi.org/10.1007/s10707-021-00439-w Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100489
in Geoinformatica > vol 26 n° 2 (April 2022) . - pp 429 - 447[article]A knowledge representation model based on the geographic spatiotemporal process / Kun Zheng in International journal of geographical information science IJGIS, vol 36 n° 4 (April 2022)
[article]
Titre : A knowledge representation model based on the geographic spatiotemporal process Type de document : Article/Communication Auteurs : Kun Zheng, Auteur ; Ming Hui Xie, Auteur ; Jin Biao Zhang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 674 - 691 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] analyse comparative
[Termes IGN] analyse diachronique
[Termes IGN] approche hiérarchique
[Termes IGN] ontologie
[Termes IGN] raisonnement spatiotemporel
[Termes IGN] représentation des connaissances
[Termes IGN] représentation du changement
[Termes IGN] représentation géographique
[Termes IGN] réseau sémantiqueRésumé : (auteur) Knowledge graphs (KGs) represent entities and relations as computable networks, which is of great value for discovering hidden knowledge and patterns. Geographic KGs mainly describe static facts and have difficulty representing changes, greatly limiting their application in geographic spatiotemporal processes. By analyzing the spatiotemporal features and evolution of geographic elements, this study presents the geographic evolutionary knowledge graph (GEKG). Its representation model has five core elements: time, geographic event (geo-event), geographic entity (geo-entity), activity and property, and defines six relations: logical, semantic, evolutionary and temporal relation, participation and inclusion. It establishes a hierarchical cubical model structure and each temporal layer extends vertically and horizontally starting with the earliest geo-event. Vertical expansion refers to the connection between different kinds of element, such as the participation relation between geo-entities and geo-events. Horizontal expansion indicates the association between the same kinds of element, such as the semantic relation between geo-entities. For different layers, the spatiotemporal differences of elements produce the evolutionary relation. Finally, the comparison of GEKG with Yet Another Great Ontology (YAGO) and Geographic Knowledge Graph (GeoKG) shows that GEKG has more advantages in representing geographic evolutionary knowledge, revealing the evolution mechanism of geographic elements and the evolutionary reasons. Numéro de notice : A2022-255 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1962527 Date de publication en ligne : 05/08/2021 En ligne : https://doi.org/10.1080/13658816.2021.1962527 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100228
in International journal of geographical information science IJGIS > vol 36 n° 4 (April 2022) . - pp 674 - 691[article]Meta-learning based hyperspectral target detection using siamese network / Yulei Wang in IEEE Transactions on geoscience and remote sensing, vol 60 n° 4 (April 2022)
[article]
Titre : Meta-learning based hyperspectral target detection using siamese network Type de document : Article/Communication Auteurs : Yulei Wang, Auteur ; Xi Chen, Auteur ; Fengchao Wang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 5527913 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification pixellaire
[Termes IGN] détection de cible
[Termes IGN] espace euclidien
[Termes IGN] filtrage numérique d'image
[Termes IGN] image hyperspectrale
[Termes IGN] réseau neuronal siamois
[Termes IGN] tripletRésumé : (auteur) When predicting data for which limited supervised information is available, hyperspectral target detection methods based on deep transfer learning expect that the network will not require considerable retraining to generalize to unfamiliar application contexts. Meta-learning is an effective and practical framework for solving this problem in deep learning. This article proposes a new meta-learning based hyperspectral target detection using Siamese network (MLSN). First, a deep residual convolution feature embedding module is designed to embed spectral vectors into the Euclidean feature space. Then, the triplet loss is used to learn the intraclass similarity and interclass dissimilarity between spectra in embedding feature space by using the known labeled source data on the designed three-channel Siamese network for meta-training. The learned meta-knowledge is updated with the prior target spectrum through a designed two-channel Siamese network to quickly adapt to the new detection task. It should be noted that the parameters and structure of the deep residual convolution embedding modules of each channel in the Siamese network are identical. Finally, the spatial information is combined, and the detection map of the two-channel Siamese network is processed by the guiding image filtering and morphological closing operation, and a final detection result is obtained. Based on the experimental analysis of six real hyperspectral image datasets, the proposed MLSN has shown its excellent comprehensive performance. Numéro de notice : A2022-381 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2022.3169970 Date de publication en ligne : 22/04/2022 En ligne : https://doi.org/10.1109/TGRS.2022.3169970 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100649
in IEEE Transactions on geoscience and remote sensing > vol 60 n° 4 (April 2022) . - n° 5527913[article]MTLM: a multi-task learning model for travel time estimation / Saijun Xu in Geoinformatica, vol 26 n° 2 (April 2022)
[article]
Titre : MTLM: a multi-task learning model for travel time estimation Type de document : Article/Communication Auteurs : Saijun Xu, Auteur ; Ruoqian Zhang, Auteur ; Wanjun Cheng, Auteur ; Jiajie Xu, Auteur Année de publication : 2022 Article en page(s) : pp 379 - 395 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] analyse coût-avantage
[Termes IGN] apprentissage automatique
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] durée de trajet
[Termes IGN] modèle de simulation
[Termes IGN] transport collectif
[Termes IGN] transport intermodalRésumé : (auteur) Travel time estimation (TTE) is an important research topic in many geographic applications for smart city research. However, existing approaches either ignore the impact of transportation modes, or assume the mode information is known for each training trajectory and the query input. In this paper, we propose a multi-task learning model for travel time estimation called MTLM, which recommends the appropriate transportation mode for users, and then estimates the related travel time of the path. It integrates transportation-mode recommendation task and travel time estimation task to capture the mutual influence between them for more accurate TTE results. Furthermore, it captures spatio-temporal dependencies and transportation mode effect by learning effective representations for TTE. It combines the transportation-mode recommendation loss and TTE loss for training. Extensive experiments on real datasets demonstrate the effectiveness of our proposed methods. Numéro de notice : A2022-325 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : https://doi.org/10.1007/s10707-020-00422-x Date de publication en ligne : 15/08/2020 En ligne : https://doi.org/10.1007/s10707-020-00422-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100488
in Geoinformatica > vol 26 n° 2 (April 2022) . - pp 379 - 395[article]PolGAN: A deep-learning-based unsupervised forest height estimation based on the synergy of PolInSAR and LiDAR data / Qi Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 186 (April 2022)PermalinkRegularized integer least-squares estimation: Tikhonov’s regularization in a weak GNSS model / Zemin Wu in Journal of geodesy, vol 96 n° 4 (April 2022)PermalinkResearch on machine intelligent perception of urban geographic location based on high resolution remote sensing images / Jun Chen in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 4 (April 2022)PermalinkSimulating future LUCC by coupling climate change and human effects based on multi-phase remote sensing data / Zihao Huang in Remote sensing, vol 14 n° 7 (April-1 2022)PermalinkSpatially oriented convolutional neural network for spatial relation extraction from natural language texts / Qinjun Qiu in Transactions in GIS, vol 26 n° 2 (April 2022)PermalinkSpecies level classification of Mediterranean sparse forests-maquis formations using Sentinel-2 imagery / Semiha Demirbaş Çağlayana in Geocarto international, vol 37 n° 6 ([01/04/2022])PermalinkUncertainty estimation for stereo matching based on evidential deep learning / Chen Wang in Pattern recognition, vol 124 (April 2022)PermalinkVD-LAB: A view-decoupled network with local-global aggregation bridge for airborne laser scanning point cloud classification / Jihao Li in ISPRS Journal of photogrammetry and remote sensing, vol 186 (April 2022)PermalinkAboveground biomass of salt-marsh vegetation in coastal wetlands: Sample expansion of in situ hyperspectral and Sentinel-2 data using a generative adversarial network / Chen Chen in Remote sensing of environment, vol 270 (March 2022)PermalinkAccessing spatial knowledge networks with maps / Markus Jobst in International journal of cartography, vol 8 n° 1 (March 2022)Permalink