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Building footprint extraction in Yangon city from monocular optical satellite image using deep learning / Hein Thura Aung in Geocarto international, vol 37 n° 3 ([01/02/2022])  

Public [article]  inGeocarto international > vol 37 n° 3 [01/02/2022] . - pp 792 - 812 Titre : Building footprint extraction in Yangon city from monocular optical satellite image using deep learning Type de document : Article/Communication Auteurs : Hein Thura Aung, Auteur ; Sao Hone Pha, Auteur ; Wataru Takeuchi, Auteur Année de publication : 2022 Article en page(s) : pp 792 - 812 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] Birmanie [Termes IGN] détection du bâti [Termes IGN] empreinte [Termes IGN] image Geoeye [Termes IGN] image isolée [Termes IGN] réseau antagoniste génératif [Termes IGN] vision monoculaire Résumé : (auteur) In this research, building footprints in Yangon City, Myanmar are extracted only from monocular optical satellite image by using conditional generative adversarial network (CGAN). Both training dataset and validating dataset are created from GeoEYE image of Dagon Township in Yangon City. Eight training models are created according to the change of values in three training parameters; learning rate, β1 term of Adam, and number of filters in the first convolution layer of the generator and the discriminator. The images of the validating dataset are divided into four image groups; trees, buildings, mixed trees and buildings, and pagodas. The output images of eight trained models are transformed to the vector images and then evaluated by comparing with manually digitized polygons using completeness, correctness and F1 measure. According to the results, by using CGAN, building footprints can be extracted up to 71% of completeness, 81% of correctness and 69% of F1 score from only monocular optical satellite image. Numéro de notice : A2022-345 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1740949 Date de publication en ligne : 20/03/2020 En ligne : https://doi.org/10.1080/10106049.2020.1740949 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100526 [article]

Uncertainty management for robust probabilistic change detection from multi-temporal Geoeye-1 imagery / Mahmoud Salah in Applied geomatics, vol 13 n° 2 (June 2021)  

Public [article]  inApplied geomatics > vol 13 n° 2 (June 2021) . - pp 261 - 275 Titre : Uncertainty management for robust probabilistic change detection from multi-temporal Geoeye-1 imagery Type de document : Article/Communication Auteurs : Mahmoud Salah, Auteur Année de publication : 2021 Article en page(s) : pp 261 - 275 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] appariement d'histogramme [Termes IGN] champ aléatoire de Markov [Termes IGN] classification dirigée [Termes IGN] classification par maximum de vraisemblance [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] détection de changement [Termes IGN] géoréférencement [Termes IGN] Giza [Termes IGN] image à très haute résolution [Termes IGN] image Geoeye [Termes IGN] image multitemporelle [Termes IGN] incertitude des données [Termes IGN] méthode robuste [Termes IGN] modèle de Markov caché [Termes IGN] occupation du sol [Termes IGN] réseau neuronal artificiel [Termes IGN] utilisation du sol Résumé : (auteur) Robust approaches for image change detection (ICD) are essential for a range of large-scale applications. However, the uncertainties involved in such approaches have not been fully addressed. To investigate this problem, this paper proposes a new approach for change detection from multi-temporal very high resolution (VHR) satellite imagery based on uncertainty detection and management. First, two GeoEye-1 images of Giza urban area (Egypt), acquired in 2009 and 2019, have been geographically co-registered and their histograms have been matched. Second, a set of feature attributes have been generated from the co-registered images. Third, the support vector machine (SVM) algorithm has been adopted to classify the data into four classes: building, tree, road, and ground. In this regard, the co-registered images along with the generated attributes have been applied as input data for the SVM to calculate the probability of each pixel belonging to each class. After that, the probability images for both epochs have been compared to model the uncertainty of changes. The uncertainty places are then evaluated to estimate their likelihood of being change or no change. Finally, the obtained results have been compared with manually digitized change detection map. Compared with using the widely used post-classification comparison (PCC) approach, the results suggest that (1) the proposed method has improved the overall accuracy of change detection by 13%; (2) the class-accuracies have been improved by 35.63%; and (3) the achieved accuracies for the proposed approach are less variable. Whereas the standard deviation (SD) of the accuracies obtained for the proposed approach is 6.80, the SD of those obtained for the PCC approach is 35.50. Numéro de notice : A2021-412 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s12518-020-00346-z Date de publication en ligne : 28/10/2020 En ligne : https://doi.org/10.1007/s12518-020-00346-z Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97737 [article]

Pan-sharpening via multiscale dynamic convolutional neural network / Jianwen Hu in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 3 (March 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > Vol 59 n° 3 (March 2021) . - pp 2231 - 2244 Titre : Pan-sharpening via multiscale dynamic convolutional neural network Type de document : Article/Communication Auteurs : Jianwen Hu, Auteur ; Pei Hu, Auteur ; Xudong Kang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 2231 - 2244 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] données multiéchelles [Termes IGN] image Geoeye [Termes IGN] image multibande [Termes IGN] image panchromatique [Termes IGN] image Quickbird [Termes IGN] image Worldview [Termes IGN] pansharpening (fusion d'images) [Termes IGN] reconstruction d'image Résumé : (Auteur) Pan-sharpening is an effective method to obtain high-resolution multispectral images by fusing panchromatic (PAN) images with fine spatial structure and low-resolution multispectral images with rich spectral information. In this article, a multiscale pan-sharpening method based on dynamic convolutional neural network is proposed. The filters in dynamic convolution are generated dynamically and locally by the filter generation network which is different from the standard convolution and strengthens the adaptivity of the network. The dynamic filters are adaptively changed according to the input images. The proposed multiscale dynamic convolutions extract detail feature of PAN image at different scales. Multiscale network structure is beneficial to obtain effective detail features. The weights obtained by the weight generation network are used to adjust the relationship among the detail features in each scale. The GeoEye-1, QuickBird, and WorldView-3 data are used to evaluate the performance of the proposed method. Compared with the widely used state-of-the-art pan-sharpening approaches, the experimental results demonstrate the superiority of the proposed method in terms of both objective quality indexes and visual performance. Numéro de notice : A2021-216 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3007884 Date de publication en ligne : 16/07/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3007884 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97206 [article]

An improved approach based on terrain-dependent mathematical models for georeferencing pushbroom satellite images / Behrooz Moradi in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 1 (January 2021)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 1 (January 2021) . - pp 53 - 69 Titre : An improved approach based on terrain-dependent mathematical models for georeferencing pushbroom satellite images Type de document : Article/Communication Auteurs : Behrooz Moradi, Auteur ; Mohammad Javad Valadan Zoej, Auteur ; Sayad Yaghoobi, Auteur ; Somayeh Yavari, Auteur Année de publication : 2021 Article en page(s) : pp 53 - 69 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] géoréférencement [Termes IGN] image à haute résolution [Termes IGN] image Geoeye [Termes IGN] image Ikonos [Termes IGN] Iran [Termes IGN] modèle géométrique de prise de vue [Termes IGN] modèle par fonctions rationnelles [Termes IGN] modélisation 3D Résumé : (Auteur) Recently, linear features in remotely sensed imagery have gained much attention because of their unique characteristics compared to other control features. For georeferencing high-resolution satellite images, the observations in the mathematical equations (slope and y-intercept) of the corresponding control lines in the two spaces are considered the same based on recent studies. However, the use of such assumptions causes error and reduces the accuracy of registration. The aim of this article is to present a methodology based on a quasi-observation assumption in the mathematical equations in the process of georeferencing. Experimental results for IKONOS and GeoEye images over two different cities of Iran indicate that the quasi-observation assumption can increase the average registration accuracy up to 48.96% and 24.77% using 3D-affine and rational function models, respectively. This improvement in accuracy increases the processing time by 31.48% over traditional approaches; however, the proposed methodology can be regarded as an efficient solution. Numéro de notice : A2021-057 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.1.53 Date de publication en ligne : 01/01/2021 En ligne : https://doi.org/10.14358/PERS.87.1.53 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96768 [article]

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Déterminants de la composition floristique et estimations des stocks de carbone des peuplements forestiers matures de Uma (Tshopo, RDC) / John Katembo Mukirania (2021)  

Public Titre : Déterminants de la composition floristique et estimations des stocks de carbone des peuplements forestiers matures de Uma (Tshopo, RDC) Titre original : Determinants of floristic composition and estimates of carbon stocks in mature forest stands in Uma (Tshopo, DRC) Type de document : Thèse/HDR Auteurs : John Katembo Mukirania, Auteur ; Faustin Boyemba Bosela, Directeur de thèse ; Nicolas Barbier, Directeur de thèse Editeur : Montpellier : Centre de Coopération Internationale en recherche agronomique pour le Développement CIRAD Année de publication : 2021 Note générale : bibliographie thèse soutenue le 30 mars 2021 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] biomasse forestière [Termes IGN] composition floristique [Termes IGN] Congo [Termes IGN] dynamique de la végétation [Termes IGN] image Geoeye [Termes IGN] puits de carbone Résumé : (auteur) The study of tree assemblages in tropical forests is gaining new impetus with the need to assess carbon emissions at high precision and resolution, while limiting the erosion of diversity and promoting sustainable forest management. The objective of this study was to (i) investigate the respective roles of topographic / soil gradients and endogenous dynamics in shaping local variations in dominance; (ii) demonstrate the feasibility of studying canopy texture by harmonizing Fourier-based Textural Ordination (FOTO) indices of two GeoEye - 50 cm images, acquired from different phenologic seasons, to calibrate AGB inversion model using inventory plots. The study was conducted in Uma forest, East of Kisangani, Democratic Republic of Congo. Dataset of 30 1-ha plots, in which all trees above 10 cm diameter at 1.30 m height (DBH) were measured and identified. Standard physical and chemical properties of soil samples were determined (macro-nutrients, textural classes and pH) and a digital elevation model (SRTM 30 m) was used to infer relevant topographical features (altitude and hydromorphy). The forest in the study area is characterized by variations in the abundance of three dominant species: Petersianthus macrocarpus (P. BEAUV.) LIBEN, Gilbertiodendron dewevrei (De Wild.) J. Léonard and Julbernardia seretii (DE WILD.) TROUPIN, one non-pioneer, light demanding species and two late successional, shade tolerant species respectively. These variations occur nearly independently of variations in the substratum or topography, despite important gradients of the range of considered variables. Analyzing differential relative abundance of the three dominant species in the lower strata and in the canopy, did not provide evidence of shifts in dominance, in which a species would obviously tend to replace another through time in any of the three floristic groups. This suggests that in this study area the states of dominance in the vegetation are stable across generations, that successional dynamics are very slow or that they are localized to peculiar locations. Using FOTO method, this study documents a strong relation between observed and predicted AGBs, without cross validation (R² of the linear regression reached 0.82 (mean square error = 27.24 T/ha). This correlation was still present, although weaker, with cross validation (R² of the linear regression between observed and predicted AGBs = 0.64). The mean square error increases to 46.68 T/ha after cross validation for a mean of 450 T/ha. This result confirms the potential of FOTO indices of optical very high resolution satellite images to quantify aboveground biomass without no signal saturation in high AGB tropical forests. Numéro de notice : 17670 Affiliation des auteurs : non IGN Thématique : FORET Nature : Thèse étrangère Note de thèse : thèse : Ecologie et gestion des ressources végétales : Kisangani (République Démocratique du Congo) : 2021 Organisme de stage : UMR AMAP - Botanique et Modélisation de l'Architecture des Plantes et des Végétations nature-HAL : Thèse DOI : sans En ligne : https://hal.archives-ouvertes.fr/tel-03268307 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97979

A review of image fusion techniques for pan-sharpening of high-resolution satellite imagery / Farzaneh Dadrass Javan in ISPRS Journal of photogrammetry and remote sensing, vol 171 (January 2021)  

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A framework for unsupervised wildfire damage assessment using VHR satellite images with PlanetScope data / Minkyung Chung in Remote sensing, vol 12 n° 22 (December-1 2020)  

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Distribution spatiale et dynamique de la population de palmiers rôniers, Borassus aethiopum Mart., par approche de la télédétection et du Système d’Information Géographique (SIG) de la réserve de Lamto (Centre de la Côte d’Ivoire) / Kouakou Guy-Casimir Douffi (2020)  

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A two-scale approach for estimating forest aboveground biomass with optical remote sensing images in a subtropical forest of Nepal / Upama A. Koju in Journal of Forestry Research, vol 30 n° 6 (December 2019)  

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Unsupervised-restricted deconvolutional neural network for very high resolution remote-sensing image classification / Yiting Tao in IEEE Transactions on geoscience and remote sensing, vol 55 n° 12 (December 2017)  

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RPC-based coregistration of VHR imagery for urban change detection / Shabnam Jabari in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 7 (juillet 2016)  

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A feature selection approach for segmentation of very high-resolution satellite images / Ahmad Izadipour in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 3 (March 2016)  

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Distinctive order based self-similarity descriptor for multi-sensor remote sensing image matching / Amin Sedaghat in ISPRS Journal of photogrammetry and remote sensing, vol 108 (October 2015)  

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MTF-adjusted pansharpening approach based on coupled multiresolution decompositions / Abdelaziz Kallel in IEEE Transactions on geoscience and remote sensing, vol 53 n° 6 (June 2015)  

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Radiometric and geometric evaluation of GeoEye-1, WorldView-2 and Pléiades-1A stereo images for 3D information extraction / Daniela Poli in ISPRS Journal of photogrammetry and remote sensing, vol 100 (February 2015)  

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