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Three-dimensional building change detection using object-based image analysis (case study: Tehran) / Fatemeh Tabib Mahmoudi in Applied geomatics, vol 13 n° 3 (September 2021)
[article]
Titre : Three-dimensional building change detection using object-based image analysis (case study: Tehran) Type de document : Article/Communication Auteurs : Fatemeh Tabib Mahmoudi, Auteur ; Sharareh Hosseini, Auteur Année de publication : 2021 Article en page(s) : pp 325 - 332 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] analyse diachronique
[Termes IGN] Bâti-3D
[Termes IGN] détection de changement
[Termes IGN] détection du bâti
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] gestion urbaine
[Termes IGN] hauteur du bâti
[Termes IGN] Matlab
[Termes IGN] modèle 3D de l'espace urbain
[Termes IGN] modèle numérique de surface
[Termes IGN] segmentation d'image
[Termes IGN] TéhéranRésumé : (auteur) Natural disasters such as earthquakes and floods together with the urban sprawl conducted by increasing the population make multi-temporal changes in building areas. Destruction, buildings’ renovation, and constructing new buildings are the main changes of the urban areas that should be detected to update three-dimensional city models. The results of performing three-dimensional changes detecting of high altitude objects such as buildings are more close to reality than the two-dimensional methods. In this study, a three-dimensional changes detection method is proposed based on digital elevation models (DEMs). In the first step of this proposed method, the normalized digital surface model (nDSM) is generated for timely datasets. Then, object-based image analysis is utilized by performing segmentation followed by the structural classification of DEMs. Differencing and comparing the multi-temporal classification maps as the third step of the proposed algorithm led to analyzing the occurred changes. The obtained results are evaluated in an urban area in Tehran, Iran, in a 9-year time interval. These results represent −9.7% decreasing rate in low-rise buildings and also −1.37% decreases in the ground. Moreover, the class of high-rise buildings increased for +16.4% which conforms to making new constructions in addition to the renovation of low-rise buildings. According to the area analyzing of the changes, 4.8% of the investigated study area has new constructions, 3.05% has buildings’ renovation, and 3.89% has destruction in that 9-year period. Numéro de notice : A2021-623 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s12518-020-00349-w Date de publication en ligne : 07/01/2021 En ligne : https://doi.org/10.1007/s12518-020-00349-w Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98247
in Applied geomatics > vol 13 n° 3 (September 2021) . - pp 325 - 332[article]Utilisation de l'apprentissage profond dans la modélisation 3D urbaine [Partie 1] / Hamza Ben Addou in Géomatique expert, n° 135 (septembre 2021)
[article]
Titre : Utilisation de l'apprentissage profond dans la modélisation 3D urbaine [Partie 1] Type de document : Article/Communication Auteurs : Hamza Ben Addou, Auteur Année de publication : 2021 Article en page(s) : pp 11 - 20 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection du bâti
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] emprise au sol
[Termes IGN] fusion de données multisource
[Termes IGN] image aérienne
[Termes IGN] information sémantique
[Termes IGN] modèle 3D de l'espace urbain
[Termes IGN] segmentation d'image
[Termes IGN] semis de pointsRésumé : (Auteur) Partie 1 : Mise en place d’un processus de détection automatique des emprises de bâtiments par apprentissage profond Numéro de notice : A2021-660 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE/URBANISME Nature : Article nature-HAL : ArtSansCL DOI : sans Date de publication en ligne : 01/09/2021 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98414
in Géomatique expert > n° 135 (septembre 2021) . - pp 11 - 20[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité IFN-001-P002273 PER Revue Nogent-sur-Vernisson Salle périodiques Exclu du prêt Connecting images through sources: Exploring low-data, heterogeneous instance retrieval / Dimitri Gominski in Remote sensing, vol 13 n° 16 (August-2 2021)
[article]
Titre : Connecting images through sources: Exploring low-data, heterogeneous instance retrieval Type de document : Article/Communication Auteurs : Dimitri Gominski , Auteur ; Valérie Gouet-Brunet , Auteur ; Liming Chen, Auteur Année de publication : 2021 Projets : Alegoria / Gouet-Brunet, Valérie Article en page(s) : n° 3080 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] description multiniveau
[Termes IGN] patrimoine culturel
[Termes IGN] recherche d'image basée sur le contenu
[Termes IGN] test de performanceRésumé : (auteur) Along with a new volume of images containing valuable information about our past, the digitization of historical territorial imagery has brought the challenge of understanding and interconnecting collections with unique or rare representation characteristics, and sparse metadata. Content-based image retrieval offers a promising solution in this context, by building links in the data without relying on human supervision. However, while the latest propositions in deep learning have shown impressive results in applications linked to feature learning, they often rely on the hypothesis that there exists a training dataset matching the use case. Increasing generalization and robustness to variations remains an open challenge, poorly understood in the context of real-world applications. Introducing the alegoria benchmark, containing multi-date vertical and oblique aerial digitized photography mixed with more modern street-level pictures, we formulate the problem of low-data, heterogeneous image retrieval, and propose associated evaluation setups and measures. We propose a review of ideas and methods to tackle this problem, extensively compare state-of-the-art descriptors and propose a new multi-descriptor diffusion method to exploit their comparative strengths. Our experiments highlight the benefits of combining descriptors and the compromise between absolute and cross-domain performance. Numéro de notice : A2021-610 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs13163080 Date de publication en ligne : 05/08/2021 En ligne : https://doi.org/10.3390/rs13163080 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98357
in Remote sensing > vol 13 n° 16 (August-2 2021) . - n° 3080[article]Monitoring forest disturbance using time-series MODIS NDVI in Michoacán, Mexico / Yao Gao in Geocarto international, vol 36 n° 15 ([15/08/2021])
[article]
Titre : Monitoring forest disturbance using time-series MODIS NDVI in Michoacán, Mexico Type de document : Article/Communication Auteurs : Yao Gao, Auteur ; Alexander Quevedo, Auteur ; Zoltan Szantoi, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1768 - 1784 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] détection de changement
[Termes IGN] fonction harmonique
[Termes IGN] image Terra-MODIS
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] Mexique
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] série temporelle
[Termes IGN] surveillance forestière
[Termes IGN] variation saisonnièreRésumé : (auteur) MODIS-based NDVI time series (2000–2016) was applied to monitor sub-annual forest disturbance in the Mexican state of Michoacán, with an algorithm that decomposes the time-series data into a harmonic function and a trend. To detect change, a moving sum of residuals between the observed and predicted NDVI values was compared with that from the reference period. Magnitude of change was computed by subtracting the predicted NDVI from the observed one. By comparing the detected changes with reference data through visual interpretation, a threshold of |0.05| was established as the magnitude of change for forest disturbance detection. The method detected more forest gain than loss for 2013–2016, a result which is supported by recent findings from the national forest inventory. Forest loss decreases yearly for 2013–2016, and forest gain peaks at 2014 and 2015. We verified the findings with data from the global forest cover change project. Numéro de notice : A2021-580 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1661032 Date de publication en ligne : 09/09/2019 En ligne : https://doi.org/10.1080/10106049.2019.1661032 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98185
in Geocarto international > vol 36 n° 15 [15/08/2021] . - pp 1768 - 1784[article]Background segmentation in multicolored illumination environments / Nikolas Ladas in The Visual Computer, vol 37 n° 8 (August 2021)
[article]
Titre : Background segmentation in multicolored illumination environments Type de document : Article/Communication Auteurs : Nikolas Ladas, Auteur ; Paris Kaimakis, Auteur ; Yiorgos Chrysanthou, Auteur Année de publication : 2021 Article en page(s) : pp 2221 - 2233 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification pixellaire
[Termes IGN] détection d'ombre
[Termes IGN] éclairage
[Termes IGN] éclairement lumineux
[Termes IGN] modèle stochastique
[Termes IGN] objectif grand angulaire
[Termes IGN] réflectance
[Termes IGN] segmentation d'imageRésumé : (auteur) We present an algorithm for the segmentation of images into background and foreground regions. The proposed algorithm utilizes a physically based formulation of scene appearance which explicitly models the formation of shadows originating from color light sources. This formulation enables a probabilistic model to distinguish between shadows and foreground objects in challenging images. A key component of the proposed method is an algorithm for estimating the illumination arriving at the scene. We evaluate our algorithm using synthetic and real-world data and show that the proposed method performs favorably against other commonly used segmentation methods. Numéro de notice : A2021-596 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00371-020-01981-8 Date de publication en ligne : 06/10/2020 En ligne : https://doi.org/10.1007/s00371-020-01981-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98225
in The Visual Computer > vol 37 n° 8 (August 2021) . - pp 2221 - 2233[article]Mapping essential urban land use categories with open big data: Results for five metropolitan areas in the United States of America / Bin Chen in ISPRS Journal of photogrammetry and remote sensing, vol 178 (August 2021)PermalinkRapid and large-scale mapping of flood inundation via integrating spaceborne synthetic aperture radar imagery with unsupervised deep learning / Xin Jiang in ISPRS Journal of photogrammetry and remote sensing, vol 178 (August 2021)PermalinkComNet: combinational neural network for object detection in UAV-borne thermal images / Minglei Li in IEEE Transactions on geoscience and remote sensing, vol 59 n° 8 (August 2021)PermalinkComparison of classification methods for urban green space extraction using very high resolution worldview-3 imagery / S. Vigneshwaran in Geocarto international, vol 36 n° 13 ([15/07/2021])PermalinkDetecting structural changes induced by Heterobasidion root rot on Scots pines using terrestrial laser scanning / Timo P Pitkänen in Forest ecology and management, vol 492 (July-15 2021)PermalinkCNN-based RGB-D salient object detection: Learn, select, and fuse / Hao Chen in International journal of computer vision, vol 129 n° 7 (July 2021)PermalinkDigital camera calibration for cultural heritage documentation: the case study of a mass digitization project of religious monuments in Cyprus / Evagoras Evagorou in European journal of remote sensing, vol 54 sup 1 (2021)PermalinkFlood depth mapping in street photos with image processing and deep neural networks / Bahareh Alizadeh Kharazi in Computers, Environment and Urban Systems, vol 88 (July 2021)PermalinkFluvial gravel bar mapping with spectral signal mixture analysis / Liza Stančič in European journal of remote sensing, vol 54 sup 1 (2021)PermalinkGlacier elevation change in the Western Qilian mountains as observed by TerraSAR-X/TanDEM-X images / Qibing Zhang in Geocarto international, vol 36 n° 12 ([01/07/2021])Permalink