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A relation-augmented embedded graph attention network for remote sensing object detection / Shu Tian in IEEE Transactions on geoscience and remote sensing, vol 60 n° 10 (October 2022)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 60 n° 10 (October 2022) . - n° 1000718 Titre : A relation-augmented embedded graph attention network for remote sensing object detection Type de document : Article/Communication Auteurs : Shu Tian, Auteur ; Lihong Kang, Auteur ; Xiangwei Xing, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 1000718 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] attention (apprentissage automatique) [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection d'objet [Termes IGN] graphe [Termes IGN] image à haute résolution [Termes IGN] information sémantique [Termes IGN] relation sémantique [Termes IGN] relation spatiale [Termes IGN] réseau neuronal de graphes [Termes IGN] SIFT (algorithme) Résumé : (auteur) Multiclass geospatial object detection in high spatial resolution remote sensing imagery (HSRI) is still a challenging task. The main reason is that the objects in HRSI are location-variable and semantic-confusable, which results in the difficulties in differentiating the complicated spatial patterns and deriving the implicitly semantic labels among different categories of objects. In this article, we propose a relation-augmented embedded graph attention network (EGAT), which enables the full exploitation of the underlying spatial and semantic relations among objects for improving the detection performance. Specifically, we first construct two sets of spatial and semantic graphs of objects–objects for object relations modeling. Second, a Siamese architecture-based embedding spatial and semantic graph attention network is designed for relations reasoning, which is implemented by introducing the long short-term memory (LSTM) mechanism into the EGAT, for learning the relations among different categories of intraobjects and interobjects. Driven by the spatial and semantic LSTM, the EGAT-LSTM can adaptively focus on the critical information of reason graphs for spatial–semantic correlation discrimination in the embedding non-Euclidean feature space. By this way, the EGAT-LSTM can effectively capture the global and local spatial–semantic relationships of objects–objects, and then produce relations-augmented features for improving the performance of object detection. We conduct comprehensive experiments on three public datasets for multiclass geospatial object detection. Our method achieves state-of-the-art performance, which demonstrates the superiority and effectiveness of the proposed method. Numéro de notice : A2022-766 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2021.3073269 Date de publication en ligne : 18/05/2021 En ligne : https://doi.org/10.1109/TGRS.2021.3073269 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101788 [article]

Adaptive transfer of color from images to maps and visualizations / Mingguang Wu in Cartography and Geographic Information Science, Vol 49 n° 4 (July 2022)  

Public [article]  inCartography and Geographic Information Science > Vol 49 n° 4 (July 2022) . - pp 289 - 312 Titre : Adaptive transfer of color from images to maps and visualizations Type de document : Article/Communication Auteurs : Mingguang Wu, Auteur ; Yanjie Sun, Auteur ; Yaqian Li, Auteur Année de publication : 2022 Article en page(s) : pp 289 - 312 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] amélioration des couleurs [Termes IGN] couleur (rédaction cartographique) [Termes IGN] données vectorielles [Termes IGN] esthétique cartographique [Termes IGN] orthoimage couleur [Termes IGN] relation sémantique [Termes IGN] saillance [Termes IGN] visualisation cartographique [Vedettes matières IGN] Géovisualisation Résumé : (auteur) Because crafting attractive and effective colors from scratch is a high-effort and time-consuming process in map and visualization design, transferring color from an inspiration source to maps and visualizations is a promising technique for both novices and experts. To date, existing image-to-image color transfer methods suffer from ambiguities and inconsistencies; no computational approach is available to transfer color from arbitrary images to vector maps. To fill this gap, we propose a computational method that transfers color from arbitrary images to a vector map. First, we classify reference images into regions with measures of saliency. Second, we quantify the communicative quality and esthetics of colors in maps; we then transform the problem of color transfer into a dual-objective, multiple-constraint optimization problem. We also present a solution method that can create a series of optimal color suggestions and generate a communicative quality-esthetic compromise solution. We compare our method with an image-to-image method based on two sample maps and six reference images. The results indicate that our method is adaptive to mapping scales, themes, and regions. The evaluation also provides preliminary evidence that our method can achieve better communicative quality and harmony. Numéro de notice : A2022-478 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2021.1982009 Date de publication en ligne : 10/11/2021 En ligne : https://doi.org/10.1080/15230406.2021.1982009 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100826 [article]

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]

Geographic knowledge graph attribute normalization: Improving the accuracy by fusing optimal granularity clustering and co-occurrence analysis / Chuan Yin in ISPRS International journal of geo-information, vol 11 n° 7 (July 2022)  

Public [article]  inISPRS International journal of geo-information > vol 11 n° 7 (July 2022) . - n° 360 Titre : Geographic knowledge graph attribute normalization: Improving the accuracy by fusing optimal granularity clustering and co-occurrence analysis Type de document : Article/Communication Auteurs : Chuan Yin, Auteur ; Binyu Zhang, Auteur ; Wanzeng Liu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 360 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] analyse de groupement [Termes IGN] attribut sémantique [Termes IGN] granularité (informatique) [Termes IGN] granularité d'image [Termes IGN] matrice de co-occurrence [Termes IGN] plus proche voisin, algorithme du [Termes IGN] relation sémantique [Termes IGN] réseau sémantique [Termes IGN] synonymie Résumé : (auteur) Expansion of the entity attribute information of geographic knowledge graphs is essentially the fusion of the Internet’s encyclopedic knowledge. However, it lacks structured attribute information, and synonymy and polysemy always exist. These reduce the quality of the knowledge graph and cause incomplete and inaccurate semantic retrieval. Therefore, we normalize the attributes of a geographic knowledge graph based on optimal granularity clustering and co-occurrence analysis, and use structure and the semantic relation of the entity attributes to identify synonymy and correlation between attributes. Specifically: (1) We design a classification system for geographic attributes, that is, using a community discovery algorithm to classify the attribute names. The optimal clustering granularity is identified by the marker target detection algorithm. (2) We complete the fine-grained identification of attribute relations by analyzing co-occurrence relations of the attributes and rule inference. (3) Finally, the performance of the system is verified by manual discrimination using the case of “landscape, forest, field, lake and grass”. The results show the following: (1) The average precision of spatial relations was 0.974 and the average recall was 0.937; the average precision of data relations was 0.977 and the average recall was 0.998. (2) The average F1 for similarity results is 0.473; the average F1 for co-occurrence analysis results is 0.735; the average F1 for rule-based modification results is 0.934; the results show that the accuracy is greater than 90%. Compared to traditional methods only focusing on similarity, the accuracy of synonymous attribute recognition improves the system and we are capable of identifying near-sense attributes. Integration of our system and attribute normalization can greatly improve both the processing efficiency and accuracy. Numéro de notice : A2022-548 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11070360 Date de publication en ligne : 23/06/2022 En ligne : https://doi.org/10.3390/ijgi11070360 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101149 [article]

Toward green cartography & visualization: a semantically-enriched method of generating energy-aware color schemes for digital maps / Yangli Han in Cartography and Geographic Information Science, vol 48 n° 1 (January 2021)  

Public [article]  inCartography and Geographic Information Science > vol 48 n° 1 (January 2021) . - pp 43 - 62 Titre : Toward green cartography & visualization: a semantically-enriched method of generating energy-aware color schemes for digital maps Type de document : Article/Communication Auteurs : Yangli Han, Auteur ; Mingguang Wu, Auteur ; Robert Emmett Roth, Auteur Année de publication : 2021 Article en page(s) : pp 43 - 62 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] amélioration des couleurs [Termes IGN] cartographie pour écran mobile [Termes IGN] conception cartographique [Termes IGN] couleur (rédaction cartographique) [Termes IGN] économie d'énergie [Termes IGN] enrichissement sémantique [Termes IGN] impact sur l'environnement [Termes IGN] optimisation (mathématiques) [Termes IGN] relation sémantique [Termes IGN] téléphone intelligent [Termes IGN] visualisation cartographique [Vedettes matières IGN] Géovisualisation Résumé : (auteur) We introduce a semantically-enriched method of generating color schemes for various types of digital maps that reduces the energy consumption of the display device while preserving the quality of the original design. Energy-aware design intersects two important trends in cartography. First, as more maps are viewed today on mobile, battery life has become a central constraint influencing design. Second, there is increasing need for green computing, which encourages the efficient use of energy to limit environmental impacts. This paper focuses on one important aspect of energy-aware cartography: color design. Existing research on energy-aware color adjustment methods apply broadly to images or websites. However, the colors used in maps have more structured semantic relationships than most documents viewed on mobile devices, and efforts to account for these relationships while reducing energy consumption are limited. To fill this gap, we mathematically formalize energy-aware map-color adjustment as a constrained optimization problem: we define energy consumption as the objective function and model the preservation of semantic relationships as the search constraints. We evaluate our proposed method against a common color dimming method using four maps with different semantic relationships. The evaluation suggests that our proposed method better preserves the original color semantics. Numéro de notice : A2021-018 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2020.1827040 Date de publication en ligne : 05/11/2020 En ligne : https://doi.org/10.1080/15230406.2020.1827040 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96457 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
032-2021011	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Semantic relatedness algorithm for keyword sets of geographic metadata / Zugang Chen in Cartography and Geographic Information Science, vol 47 n° 2 (February 2020)  

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SMSM: a similarity measure for trajectory stops and moves / Andre L. Lehmann in International journal of geographical information science IJGIS, vol 33 n° 9 (September 2019)  

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An approach to measuring semantic relatedness of geographic terminologies using a thesaurus and lexical database sources / Zugang Chen in ISPRS International journal of geo-information, vol 7 n° 3 (March 2018)  

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Interpreting the fuzzy semantics of natural-language spatial relation terms with the fuzzy random forest algorithm / Xiaonan Wang in ISPRS International journal of geo-information, vol 7 n° 2 (February 2018)  

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A higher order conditional random field model for simultaneous classification of land cover and land use / Lena Albert in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)  

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Reference data enhancement for geographic information retrieval using linked data / Tiago H. V. M. Moura in Transactions in GIS, vol 21 n° 4 (August 2017)  

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Robust point cloud classification based on multi-level semantic relationships for urban scenes / Qing Zhu in ISPRS Journal of photogrammetry and remote sensing, vol 129 (July 2017)  

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Representation and discovery of building patterns: a three-level relational approach / Shihong Du in International journal of geographical information science IJGIS, vol 30 n° 5-6 (May - June 2016)  

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Localisation d’entités nommées historiques par analyse multi-critères et prise en compte des imprécisions / Sébastien Nogueira (2016)

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A semi-automatic lightweight ontology bridging for the semantic integration of cross-domain geospatial information / Jung-Hong Hong in International journal of geographical information science IJGIS, vol 29 n° 12 (December 2015)  

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