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Landslide susceptibility prediction based on image semantic segmentation / Bowen Du in Computers & geosciences, vol 155 (October 2021)
[article]
Titre : Landslide susceptibility prediction based on image semantic segmentation Type de document : Article/Communication Auteurs : Bowen Du, Auteur ; Zirong Zhao, Auteur ; Xiao Hu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 104860 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] aléa
[Termes IGN] apprentissage automatique
[Termes IGN] Chine
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] effondrement de terrain
[Termes IGN] image captée par drone
[Termes IGN] modèle de simulation
[Termes IGN] prévention des risques
[Termes IGN] segmentation sémantiqueRésumé : (auteur) Numéro de notice : A2021-683 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.cageo.2021.104860 Date de publication en ligne : 16/06/2021 En ligne : https://doi.org/10.1016/j.cageo.2021.104860 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98408
in Computers & geosciences > vol 155 (October 2021) . - n° 104860[article]Recognition of crevasses with high-resolution digital elevation models: Application of geomorphometric modeling and texture analysis / Olga T. Ishalina in Transactions in GIS, vol 25 n° 5 (October 2021)
[article]
Titre : Recognition of crevasses with high-resolution digital elevation models: Application of geomorphometric modeling and texture analysis Type de document : Article/Communication Auteurs : Olga T. Ishalina, Auteur ; Dimitri P. Bliakharskii, Auteur ; Igor V. Florinsky, Auteur Année de publication : 2021 Article en page(s) : pp 2529 - 2552 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse comparative
[Termes IGN] analyse texturale
[Termes IGN] Antarctique
[Termes IGN] crevasse
[Termes IGN] glacier
[Termes IGN] image à haute résolution
[Termes IGN] image captée par drone
[Termes IGN] modèle numérique de surface
[Termes IGN] texture d'imageRésumé : (auteur) Crevasses—cracks in glaciers and ice sheets—pose a danger to polar researchers and glaciologists. We compare the capabilities of two techniques—geomorphometric modeling and texture analysis—to recognize open and hidden crevasses using high-resolution digital elevation models (DEMs) generated from images collected by an unmanned aerial system (UAS). The first technique includes derivation of local morphometric variables; the second includes calculation of the Haralick texture features. The study area is represented by the first 30 km of a sledge route between the Progress and Vostok polar stations, East Antarctica. The UAS survey was performed by a Geoscan 201 Geodesy UAS. For the sledge route area, DEMs with resolutions of 0.25, 0.5, and 1 m were generated. Models of 12 morphometric variables and 11 texture features were derived from the DEMs. In terms of crevasse recognition, the most informative morphometric variable and texture feature was horizontal curvature and inverse difference moment, respectively. In most cases, derivation and mapping of these variables allow one to recognize crevasses wider than 3 m; narrower crevasses can be recognized for lengths from 500 m. For crevasse recognition, the geomorphometric modeling and the Haralick texture analysis can complement each other. Numéro de notice : A2021-122 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/tgis.12790 Date de publication en ligne : 06/07/2021 En ligne : https://doi.org/10.1111/tgis.12790 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99303
in Transactions in GIS > vol 25 n° 5 (October 2021) . - pp 2529 - 2552[article]Spectral reflectance estimation of UAS multispectral imagery using satellite cross-calibration method / Saket Gowravaram in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 10 (October 2021)
[article]
Titre : Spectral reflectance estimation of UAS multispectral imagery using satellite cross-calibration method Type de document : Article/Communication Auteurs : Saket Gowravaram, Auteur ; Haiyang Chao, Auteur ; Andrew Molthan, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 735 - 746 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] aéronef
[Termes IGN] distribution du coefficient de réflexion bidirectionnelle BRDF
[Termes IGN] étalonnage croisé
[Termes IGN] forêt
[Termes IGN] image captée par drone
[Termes IGN] image Landsat-8
[Termes IGN] image multibande
[Termes IGN] image proche infrarouge
[Termes IGN] Kansas (Etats-Unis ; état)
[Termes IGN] orthoimage
[Termes IGN] orthorectification
[Termes IGN] prairie
[Termes IGN] rayonnement proche infrarouge
[Termes IGN] réflectance spectraleRésumé : (Auteur) This paper introduces a satellite-based cross-calibration (SCC) method for spectral reflectance estimation of unmanned aircraft system (UAS) multispectral imagery. The SCC method provides a low-cost and feasible solution to convert high-resolution UAS images in digital numbers (DN) to reflectance when satellite data is available. The proposed method is evaluated using a multispectral data set, including orthorectified KHawk UAS DN imagery and Landsat 8 Operational Land Imager Level-2 surface reflectance (SR) data over a forest/grassland area. The estimated UAS reflectance images are compared with the National Ecological Observatory Network's imaging spectrometer (NIS) SR data for validation. The UAS reflectance showed high similarities with the NIS data for the near-infrared and red bands with Pearson's r values being 97 and 95.74, and root-mean-square errors being 0.0239 and 0.0096 over a 32-subplot hayfield. Numéro de notice : A2021-676 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.20-00091R2 En ligne : https://doi.org/10.14358/PERS.20-00091R2 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98863
in Photogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 10 (October 2021) . - pp 735 - 746[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2021101 SL Revue Centre de documentation Revues en salle Disponible Unsupervised self-adaptive deep learning classification network based on the optic nerve microsaccade mechanism for unmanned aerial vehicle remote sensing image classification / Ming Cong in Geocarto international, vol 36 n° 18 ([01/10/2021])
[article]
Titre : Unsupervised self-adaptive deep learning classification network based on the optic nerve microsaccade mechanism for unmanned aerial vehicle remote sensing image classification Type de document : Article/Communication Auteurs : Ming Cong, Auteur ; Zhiye Wang, Auteur ; Yiting Tao, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 2065 - 2084 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de groupement
[Termes IGN] chromatopsie
[Termes IGN] classification non dirigée
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] compréhension de l'image
[Termes IGN] échantillonnage d'image
[Termes IGN] filtrage numérique d'image
[Termes IGN] image captée par drone
[Termes IGN] vision
[Termes IGN] vision par ordinateurRésumé : (auteur) Unmanned aerial vehicle remote sensing images need to be precisely and efficiently classified. However, complex ground scenes produced by ultra-high ground resolution, data uniqueness caused by multi-perspective observations, and need for manual labelling make it difficult for current popular deep learning networks to obtain reliable references from heterogeneous samples. To address these problems, this paper proposes an optic nerve microsaccade (ONMS) classification network, developed based on multiple dilated convolution. ONMS first applies a Laplacian of Gaussian filter to find typical features of ground objects and establishes class labels using adaptive clustering. Then, using an image pyramid, multi-scale image data are mapped to the class labels adaptively to generate homologous reliable samples. Finally, an end-to-end multi-scale neural network is applied for classification. Experimental results show that ONMS significantly reduces sample labelling costs while retaining high cognitive performance, classification accuracy, and noise resistance—indicating that it has significant application advantages. Numéro de notice : A2021-707 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/10106049.2019.1687593 Date de publication en ligne : 07/11/2019 En ligne : https://doi.org/10.1080/10106049.2019.1687593 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98602
in Geocarto international > vol 36 n° 18 [01/10/2021] . - pp 2065 - 2084[article]Aerial and UAV images for photogrammetric analysis of Belvedere Glacier evolution in the period 1977–2019 / Carlo Lapige De Gaetani in Remote sensing, vol 13 n° 18 (September-2 2021)
[article]
Titre : Aerial and UAV images for photogrammetric analysis of Belvedere Glacier evolution in the period 1977–2019 Type de document : Article/Communication Auteurs : Carlo Lapige De Gaetani, Auteur ; Francesco Loli, Auteur ; Livio Pinto, Auteur Année de publication : 2021 Article en page(s) : n° 3787 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] analyse diachronique
[Termes IGN] changement climatique
[Termes IGN] données maillées
[Termes IGN] glacier
[Termes IGN] glaciologie
[Termes IGN] historique des données
[Termes IGN] image aérienne
[Termes IGN] image captée par drone
[Termes IGN] masque
[Termes IGN] modèle numérique de surface
[Termes IGN] Piémont (Italie)
[Termes IGN] point d'appui
[Termes IGN] restitution analogique
[Termes IGN] structure-from-motionRésumé : (auteur) Alpine glaciers are strongly suffering the consequences of the temperature rising and monitoring them over long periods is of particular interest for climate change tracking. A wide range of techniques can be successfully applied to survey and monitor glaciers with different spatial and temporal resolutions. However, going back in time to retrace the evolution of a glacier is still a challenging task. Historical aerial images, e.g., those acquired for regional cartographic purposes, are extremely valuable resources for studying the evolution and movement of a glacier in the past. This work analyzed the evolution of the Belvedere Glacier by means of structure from motion techniques applied to digitalized historical aerial images combined with more recent digital surveys, either from aerial platforms or UAVs. This allowed the monitoring of an Alpine glacier with high resolution and geometrical accuracy over a long span of time, covering the period 1977–2019. In this context, digital surface models of the area at different epochs were computed and jointly analyzed, retrieving the morphological dynamics of the Belvedere Glacier. The integration of datasets dating back to earlier times with those referring to surveys carried out with more modern technologies exploits at its full potential the information that at first glance could be thought obsolete, proving how historical photogrammetric datasets are a remarkable heritage for glaciological studies. Numéro de notice : A2021-753 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13183787 Date de publication en ligne : 21/09/2021 En ligne : https://doi.org/10.3390/rs13183787 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98745
in Remote sensing > vol 13 n° 18 (September-2 2021) . - n° 3787[article]Mapping canopy heights in dense tropical forests using low-cost UAV-derived photogrammetric point clouds and machine learning approaches / He Zhang in Remote sensing, vol 13 n° 18 (September-2 2021)PermalinkAutomatic building detection with polygonizing and attribute extraction from high-resolution images / Samitha Daranagama in ISPRS International journal of geo-information, vol 10 n° 9 (September 2021)PermalinkA comparison of ALS and dense photogrammetric point clouds for individual tree detection in radiata pine plantations / Irfan A. Iqbal in Remote sensing, vol 13 n° 17 (September-1 2021)PermalinkDetection of aspen in conifer-dominated boreal forests with seasonal multispectral drone image point clouds / Alwin A. Hardenbol in Silva fennica, vol 55 n° 4 (September 2021)PermalinkDetermining optimal photogrammetric adjustment of images obtained from a fixed-wing UAV / Karolina Pargiela in Photogrammetric record, Vol 36 n° 175 (September 2021)PermalinkLes journées de la Recherche IGN 2021 / Anonyme in Géomatique expert, n° 135 (septembre 2021)PermalinkMulti-task fully convolutional network for tree species mapping in dense forests using small training hyperspectral data / Laura Elena Cué La Rosa in ISPRS Journal of photogrammetry and remote sensing, vol 179 (September 2021)PermalinkTwo hidden layer neural network-based rotation forest ensemble for hyperspectral image classification / Laxmi Narayana Eeti in Geocarto international, vol 36 n° 16 ([01/09/2021])PermalinkUtilisation de l'apprentissage profond dans la modélisation 3D urbaine [Partie 1] / Hamza Ben Addou in Géomatique expert, n° 135 (septembre 2021)PermalinkAutomated tree-crown and height detection in a young forest plantation using mask region-based convolutional neural network (Mask R-CNN) / Zhenbang Hao in ISPRS Journal of photogrammetry and remote sensing, vol 178 (August 2021)Permalink