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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]

Characterization of forests and deforestation in Cambodia using ALOS/PALSAR observation / R. Avtar in Geocarto international, vol 27 n° 2 (March 2012)  

Public [article]  inGeocarto international > vol 27 n° 2 (March 2012) . - pp 119 - 137 Titre : Characterization of forests and deforestation in Cambodia using ALOS/PALSAR observation Type de document : Article/Communication Auteurs : R. Avtar, Auteur ; H. Sawada, Auteur ; Wataru Takeuchi, Auteur ; G. Singh, Auteur Année de publication : 2012 Article en page(s) : pp 119 - 137 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Cambodge [Termes IGN] caractérisation [Termes IGN] déboisement [Termes IGN] décomposition d'image [Termes IGN] forêt tropicale [Termes IGN] image ALOS-PALSAR [Termes IGN] image radar moirée [Termes IGN] indice de végétation [Termes IGN] sylviculture Résumé : (Auteur) In this study, we have demonstrated the capability of full polarimetric ALOS/Phased Array L-band Synthetic Aperture Radar data for the characterization of the forests and deforestation in Cambodia, to support climate change mitigation policies of Reducing Emission from Deforestation and Forest Degradation (REDD). We have observed mean backscattering coefficient (ó°), entropy (H), alpha angle (á), anisotropy (A), pedestal height (PH), Radar Vegetation Index (RVI) and Freeman–Durden three-component decomposition parameters. The observations show that the forest types and deforested areas are showing variable polarimetric and backscattering properties because of the structural difference. Evergreen forest is characterized by a high value of ó° HV (-12.96 dB) as compared with the deforested area (ó° HV=-22.2 dB). The value of polarimetric parameters such as entropy (0.93), RVI (0.91), PH (0.41) and Freeman–Durden volume scattering (0.43) is high for evergreen forest, whereas deforested area is characterized by the low values of entropy (0.36) and RVI (0.17). Based on these parameters, it is found that ó° HV, entropy, RVI and PH provide best results among other parameters. Numéro de notice : A2012-126 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2011.626081 Date de publication en ligne : 20/10/2011 En ligne : https://doi.org/10.1080/10106049.2011.626081 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31574 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2012021	RAB	Revue	Centre de documentation	En réserve L003	Disponible