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An area merging method in map generalization considering typical characteristics of structured geographic objects / Chengming Li in Cartography and Geographic Information Science, vol 48 n° 3 (May 2021)  

Public [article]  inCartography and Geographic Information Science > vol 48 n° 3 (May 2021) . - pp 210 - 224 Titre : An area merging method in map generalization considering typical characteristics of structured geographic objects Type de document : Article/Communication Auteurs : Chengming Li, Auteur ; Yong Yin, Auteur ; Pengda Wu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 210 - 224 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] Chine [Termes descripteurs IGN] conflit d'intégration [Termes descripteurs IGN] détection de contours [Termes descripteurs IGN] fusion de données [Termes descripteurs IGN] généralisation automatique de données [Termes descripteurs IGN] objet géographique zonal [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] programmation adaptée à l'objet [Termes descripteurs IGN] structure spatiale [Termes descripteurs IGN] tessellation [Termes descripteurs IGN] ville [Termes descripteurs IGN] zone tampon [Vedettes matières IGN] Généralisation Résumé : (auteur) Merging is an important operation in the map generalization of land-cover and other coverages. We define structured geographic objects as collections of adjacent areas with homogeneous semantics that are regularly arranged as spatial structures. Existing studies have concentrated on unstructured objects, which will lead to the structured ones losing part or even most of the typical characteristics during merging. Therefore, as a supplement to the existing mature merging method, a targeted method was proposed in this paper to address the merging problem of structured geographic objects. First, structured geographic objects were classified into four typical patterns, and they were identified automatically according to seven spatial structure parameters. Second, a Miter-type buffer transformation was introduced to extract the overall boundary of structured geographic objects, and areas inside the overall boundary were processed with the most appropriate merging operations for their pattern. Finally, the corresponding merged results of structured geographic objects were inserted back into the merged result of the original land-cover data by using the NOT operation, and the spatial conflicts near the boundary were adjusted. We test our method for a dataset of geographical census data for a city in China. The experimental results revealed that compared with state-of-the-art method, the proposed method produces more reasonable generalization result by effectively identifying and maintaining the typical spatial structures; moreover, the proposed method also preserves the planar tessellation characteristic of land-cover data and the balance of area variation in each land-cover class. Numéro de notice : A2021-314 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2020.1863862 date de publication en ligne : 19/02/2021 En ligne : https://doi.org/10.1080/15230406.2020.1863862 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97530 [article]

Learning deep semantic segmentation network under multiple weakly-supervised constraints for cross-domain remote sensing image semantic segmentation / Yansheng Li in ISPRS Journal of photogrammetry and remote sensing, vol 175 (May 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 175 (May 2021) . - pp 20 - 33 Titre : Learning deep semantic segmentation network under multiple weakly-supervised constraints for cross-domain remote sensing image semantic segmentation Type de document : Article/Communication Auteurs : Yansheng Li, Auteur ; Te Shi, Auteur ; Yongjun Zhang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 20 - 33 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] classification semi-dirigée [Termes descripteurs IGN] données d'apprentissage [Termes descripteurs IGN] programmation par contraintes [Termes descripteurs IGN] segmentation sémantique Résumé : (auteur) Due to its wide applications, remote sensing (RS) image semantic segmentation has attracted increasing research interest in recent years. Benefiting from its hierarchical abstract ability, the deep semantic segmentation network (DSSN) has achieved tremendous success on RS image semantic segmentation and has gradually become the mainstream technology. However, the superior performance of DSSN highly depends on two conditions: (I) massive quantities of labeled training data exist; (II) the testing data seriously resemble the training data. In actual RS applications, it is difficult to fully meet these conditions due to the RS sensor variation and the distinct landscape variation in different geographic locations. To make DSSN fit the actual RS scenario, this paper exploits the cross-domain RS image semantic segmentation task, which means that DSSN is trained on one labeled dataset (i.e., the source domain) but is tested on another varied dataset (i.e., the target domain). In this setting, the performance of DSSN is inevitably very limited due to the data shift between the source and target domains. To reduce the disadvantageous influence of data shift, this paper proposes a novel objective function with multiple weakly-supervised constraints to learn DSSN for cross-domain RS image semantic segmentation. Through carefully examining the characteristics of cross-domain RS image semantic segmentation, multiple weakly-supervised constraints include the weakly-supervised transfer invariant constraint (WTIC), weakly-supervised pseudo-label constraint (WPLC) and weakly-supervised rotation consistency constraint (WRCC). Specifically, DualGAN is recommended to conduct unsupervised style transfer between the source and target domains to carry out WTIC. To make full use of the merits of multiple constraints, this paper presents a dynamic optimization strategy that dynamically adjusts the constraint weights of the objective function during the training process. With full consideration of the characteristics of the cross-domain RS image semantic segmentation task, this paper gives two cross-domain RS image semantic segmentation settings: (I) variation in geographic location and (II) variation in both geographic location and imaging mode. Extensive experiments demonstrate that our proposed method remarkably outperforms the state-of-the-art methods under both of these settings. The collected datasets and evaluation benchmarks have been made publicly available online (https://github.com/te-shi/MUCSS). Numéro de notice : A2021-261 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.02.009 date de publication en ligne : 06/03/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.02.009 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97302 [article]

Restituer les bidonvilles de Nanterre : l’apport d’un outil de visualisation 3D à un projet de sciences sociales / Paul Lecat in Humanités numériques, n° 3 (2021)  

Public [article]  inHumanités numériques > n° 3 (2021) . - n° 1946 Titre : Restituer les bidonvilles de Nanterre : l’apport d’un outil de visualisation 3D à un projet de sciences sociales Titre original : Restoring the Shantytowns of Nanterre: Benefits of a 3D Visualisation Tool for a Social Sciences Project Type de document : Article/Communication Auteurs : Paul Lecat, Auteur ; Emile Blettery , Auteur ; Laetitia Delavoipiere, Auteur ; Frédéric Saly-Giocanti, Auteur ; Sylvaine Conord, Auteur ; Valérie Gouet-Brunet , Auteur ; Alexandre Devaux , Auteur ; Mathieu Brédif , Auteur ; Frédéric Moret, Auteur Année de publication : 2021 Projets : ITowns / Paparoditis, Nicolas, Alegoria / Gouet-Brunet, Valérie Article en page(s) : n° 1946 Note générale : bibliographie Langues : Français (fre) Descripteur : [Termes descripteurs IGN] analyse spatio-temporelle [Termes descripteurs IGN] Bidonville [Termes descripteurs IGN] image aérienne [Termes descripteurs IGN] morphologie urbaine [Termes descripteurs IGN] Nanterre [Termes descripteurs IGN] outil logiciel [Termes descripteurs IGN] sciences sociales [Termes descripteurs IGN] SIG 3D [Termes descripteurs IGN] sociologie [Termes descripteurs IGN] visualisation 3D [Vedettes matières IGN] Géovisualisation Résumé : (auteur) Au milieu des années 1950 surgissent à Nanterre les premières cabanes de fortune abritant des travailleurs algériens. Bientôt, ces baraques informelles s’agrègent et finissent par former des ensembles urbains, présentés et administrés comme des bidonvilles, et la ville de Nanterre y est alors durablement associée. Cet article se propose de revenir sur une expérience de recherche interdisciplinaire autour de cet objet d’étude. Des chercheurs en histoire et en sociologie urbaine ont collaboré avec des informaticiens de l’IGN afin d’utiliser et d’enrichir une plateforme de spatialisation et de visualisation de données hétérogènes pour documenter l’histoire de ces bidonvilles et comprendre la formation et la permanence de ces lieux dans la mémoire collective actuelle. Numéro de notice : A2021-308 Affiliation des auteurs : UGE-LaSTIG+Ext (2020- ) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueNat DOI : 10.4000/revuehn.1946 date de publication en ligne : 01/05/2021 En ligne : https://doi.org/10.4000/revuehn.1946 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97709 [article]

Unsupervised multi-level feature extraction for improvement of hyperspectral classification / Qiaoqiao Sun in Remote sensing, vol 13 n° 8 (15 April 2021)  

Public [article]  inRemote sensing > vol 13 n° 8 (15 April 2021) . - n° 1602 Titre : Unsupervised multi-level feature extraction for improvement of hyperspectral classification Type de document : Article/Communication Auteurs : Qiaoqiao Sun, Auteur ; Xuefeng Liu, Auteur ; Salah Bourennane, Auteur Année de publication : 2021 Article en page(s) : n° 1602 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] classification non dirigée [Termes descripteurs IGN] codage [Termes descripteurs IGN] convolution (signal) [Termes descripteurs IGN] déconvolution [Termes descripteurs IGN] échantillonnage d'image [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] observation multiniveaux Résumé : (auteur) Deep learning models have strong abilities in learning features and they have been successfully applied in hyperspectral images (HSIs). However, the training of most deep learning models requires labeled samples and the collection of labeled samples are labor-consuming in HSI. In addition, single-level features from a single layer are usually considered, which may result in the loss of some important information. Using multiple networks to obtain multi-level features is a solution, but at the cost of longer training time and computational complexity. To solve these problems, a novel unsupervised multi-level feature extraction framework that is based on a three dimensional convolutional autoencoder (3D-CAE) is proposed in this paper. The designed 3D-CAE is stacked by fully 3D convolutional layers and 3D deconvolutional layers, which allows for the spectral-spatial information of targets to be mined simultaneously. Besides, the 3D-CAE can be trained in an unsupervised way without involving labeled samples. Moreover, the multi-level features are directly obtained from the encoded layers with different scales and resolutions, which is more efficient than using multiple networks to get them. The effectiveness of the proposed multi-level features is verified on two hyperspectral data sets. The results demonstrate that the proposed method has great promise in unsupervised feature learning and can help us to further improve the hyperspectral classification when compared with single-level features. Numéro de notice : A2021-380 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs13081602 date de publication en ligne : 20/04/2021 En ligne : https://doi.org/10.3390/rs13081602 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97628 [article]

A novel class-specific object-based method for urban change detection using high-resolution remote sensing imagery / Ting Bai in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 4 (April 2021)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 4 (April 2021) . - pp 249-262 Titre : A novel class-specific object-based method for urban change detection using high-resolution remote sensing imagery Type de document : Article/Communication Auteurs : Ting Bai, Auteur ; Kaimin Sun, Auteur ; Wenzhuo Li, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 249-262 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] changement d'occupation du sol [Termes descripteurs IGN] classe d'objets [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] détection du bâti [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] milieu urbain [Termes descripteurs IGN] segmentation multi-échelle Résumé : (Auteur) A single-scale object-based change-detection classifier can distinguish only global changes in land cover, not the more granular and local changes in urban areas. To overcome this issue, a novel class-specific object-based change-detection method is proposed. This method includes three steps: class-specific scale selection, class-specific classifier selection, and land cover change detection. The first step combines multi-resolution segmentation and a random forest to select the optimal scale for each change type in land cover. The second step links multi-scale hierarchical sampling with a classifier such as random forest, support vector machine, gradient-boosting decision tree, or Adaboost; the algorithm automatically selects the optimal classifier for each change type in land cover. The final step employs the optimal classifier to detect binary changes and from-to changes for each change type in land cover. To validate the proposed method, we applied it to two high-resolution data sets in urban areas and compared the change-detection results of our proposed method with that of principal component analysis k-means, object-based change vector analysis, and support vector machine. The experimental results show that our proposed method is more accurate than the other methods. The proposed method can address the high levels of complexity found in urban areas, although it requires historical land cover maps as auxiliary data. Numéro de notice : A2021-332 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.4.249 date de publication en ligne : 01/04/2021 En ligne : https://doi.org/10.14358/PERS.87.4.249 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97528 [article]

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