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Comparison 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])  

Public [article]  inGeocarto international > vol 36 n° 13 [15/07/2021] . - pp 1429 - 1442 Titre : Comparison of classification methods for urban green space extraction using very high resolution worldview-3 imagery Type de document : Article/Communication Auteurs : S. Vigneshwaran, Auteur ; S. Vasantha Kumar, Auteur Année de publication : 2021 Article en page(s) : pp 1429 - 1442 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] carte de la végétation [Termes IGN] classification dirigée [Termes IGN] classification non dirigée [Termes IGN] classification orientée objet [Termes IGN] espace vert [Termes IGN] flore urbaine [Termes IGN] image à très haute résolution [Termes IGN] image Worldview [Termes IGN] New Delhi (Inde ; ville) [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] urbanisme Résumé : (auteur) Urban green space (UGS) plays a vital role in maintaining the ecological balance of a city and in ensuring healthy living of the city inhabitants. It is generally suggested that one-third of the city should be covered by green and to ensure this, the city administrators must have an accurate map of the existing UGS. Such a map would be useful to visualize the distribution of the existing green cover and also to find out the areas that can possibly be converted to UGS. Reported studies on UGS mapping have mostly used medium and high resolution images such as Landsat-TM, ETM+, Sentinel-2A, IKONOS, etc. However, studies on the use of very high resolution images for UGS extraction are very limited. The present study is a first attempt in utilizing the very high resolution Worldview-3 image for UGS extraction. Performance of different classification methods such as unsupervised, supervised, object based and normalized difference vegetation index (NDVI) were compared using the pan sharpened Worldview-3 image covering part of New Delhi in India. It was found that the unsupervised classification followed by manual recoding method showed superior performance with overall accuracy (OA) of 99% and κ coefficient of 0.98. Also, the OA achieved in the present study is the highest when compared to other reported studies on UGS extraction. The map of UGS revealed that almost 40% of the study area is covered by green which is more than the recommended value of 33% (one-third). In order to check the universality of the unsupervised classification approach in extracting UGS, Worldview-3 image covering Rio in Brazil was tested. It was found that an OA of 98% and κ coefficient of 0.95 were obtained which clearly indicate that the proposed approach would work very well in extracting UGS from any Worldview-3 imagery. Numéro de notice : A2021-553 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1665714 date de publication en ligne : 18/09/2019 En ligne : https://doi.org/10.1080/10106049.2019.1665714 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98104 [article]

Assessing land use–land cover change and soil erosion potential using a combined approach through remote sensing, RUSLE and random forest algorithm / Siddhartho Shekhar Paul in Geocarto international, vol 36 n° 4 ([01/03/2021])  

Public [article]  inGeocarto international > vol 36 n° 4 [01/03/2021] . - pp 361 - 375 Titre : Assessing land use–land cover change and soil erosion potential using a combined approach through remote sensing, RUSLE and random forest algorithm Type de document : Article/Communication Auteurs : Siddhartho Shekhar Paul, Auteur ; Jianbing Li, Auteur ; Yubao Li, Auteur ; Lei Shen, Auteur Année de publication : 2021 Article en page(s) : pp 361 - 375 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] bassin hydrographique [Termes IGN] changement d'occupation du sol [Termes IGN] classification orientée objet [Termes IGN] classification par forêts aléatoires [Termes IGN] coupe rase (sylviculture) [Termes IGN] détection de changement [Termes IGN] érosion [Termes IGN] modèle RUSLE [Termes IGN] occupation du sol [Termes IGN] qualité des eaux [Termes IGN] utilisation du sol Résumé : (auteur) Numéro de notice : A2021-161 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1614099 date de publication en ligne : 10/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1614099 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97081 [article]

An anchor-based graph method for detecting and classifying indoor objects from cluttered 3D point clouds / Fei Su in ISPRS Journal of photogrammetry and remote sensing, vol 172 (February 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 172 (February 2021) . - pp 114 - 131 Titre : An anchor-based graph method for detecting and classifying indoor objects from cluttered 3D point clouds Type de document : Article/Communication Auteurs : Fei Su, Auteur ; Haihong Zhu, Auteur ; Taoyi Chen, Auteur Année de publication : 2021 Article en page(s) : pp 114 - 131 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] adjacence [Termes IGN] appariement de graphes [Termes IGN] arc [Termes IGN] bloc d'ancrage [Termes IGN] classification orientée objet [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] jeu de données localisées [Termes IGN] méthode du maximum de vraisemblance (estimation) [Termes IGN] noeud [Termes IGN] objet 3D [Termes IGN] orientation [Termes IGN] positionnement en intérieur [Termes IGN] semis de points Résumé : (auteur) Most of the existing 3D indoor object classification methods have shown impressive achievements on the assumption that all objects are oriented in the upward direction with respect to the ground. To release this assumption, great effort has been made to handle arbitrarily oriented objects in terrestrial laser scanning (TLS) point clouds. As one of the most promising solutions, anchor-based graphs can be used to classify freely oriented objects. However, this approach suffers from missing anchor detection since valid detection relies heavily on the completeness of an anchor’s point clouds and is sensitive to missing data. This paper presents an anchor-based graph method to detect and classify arbitrarily oriented indoor objects. The anchors of each object are extracted by the structurally adjacent relationship among parts instead of the parts’ geometric metrics. In the case of adjacency, an anchor can be correctly extracted even with missing parts since the adjacency between an anchor and other parts is retained irrespective of the area extent of the considered parts. The best graph matching is achieved by finding the optimal corresponding node-pairs in a super-graph with fully connecting nodes based on maximum likelihood. The performances of the proposed method are evaluated with three indicators (object precision, object recall and object F1-score) in seven datasets. The experimental tests demonstrate the effectiveness of dealing with TLS point clouds, RGBD point clouds and Panorama RGBD point clouds, resulting in performance scores of approximately 0.8 for object precision and recall and over 0.9 for chair precision and table recall. Numéro de notice : A2021-087 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.12.007 date de publication en ligne : 29/12/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.12.007 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96852 [article]

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Building extraction from Lidar data using statistical methods / Haval Abdul-Jabbar Sadeq 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 33 - 42 Titre : Building extraction from Lidar data using statistical methods Type de document : Article/Communication Auteurs : Haval Abdul-Jabbar Sadeq, Auteur Année de publication : 2021 Article en page(s) : pp 33 - 42 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse de données [Termes IGN] classification orientée objet [Termes IGN] détection du bâti [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] étiquette [Termes IGN] extraction de traits caractéristiques [Termes IGN] Ransac (algorithme) [Termes IGN] semis de points Résumé : (Auteur) In this article, a straightforward, intuitive method for lidar data classification and building extraction, based on statistical analysis, is presented. The classification of the point cloud into ground and nonground is begun by individually testing each point within the point cloud using the statistical mean height. In this operation, various window sizes are specified, and the mean is obtained at each size. The points that are above the mean are saved and divided by the number of windows to obtain the proportion. Points are considered non-ground if their proportion is higher than the assigned threshold, and otherwise ground. An algorithm for classifying the obtained nonground point cloud into buildings and trees is also illustrated in this article. First the nonground points are labeled, then each label is tested individually. The process begins with segmenting each label. Then comes testing of whether each segment of points can be fitted within a specific plane. The label of the point cloud is considered a building if the number of segments considered as planes is larger than those considered as nonplanes; otherwise it is classified as a tree. Numéro de notice : A2021-055 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern date de publication en ligne : 01/01/2021 En ligne : https://doi.org/10.14358/PERS.87.1.33 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96760 [article]

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Bretagne, la végétation cartographiée / Marielle Mayo in Géomètre, n° 2185 (novembre 2020)

Public [article]  inGéomètre > n° 2185 (novembre 2020) . - pp 46 - 49 Titre : Bretagne, la végétation cartographiée Type de document : Article/Communication Auteurs : Marielle Mayo, Auteur Année de publication : 2020 Article en page(s) : pp 46 - 49 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Cartographie thématique [Termes IGN] 1:25.000 [Termes IGN] acquisition d'images [Termes IGN] aménagement régional [Termes IGN] appariement semi-automatique [Termes IGN] ArcGIS [Termes IGN] BD ortho [Termes IGN] Bretagne [Termes IGN] carte de la végétation [Termes IGN] classification orientée objet [Termes IGN] données localisées [Termes IGN] données publiques [Termes IGN] IGN cité [Termes IGN] image infrarouge couleur [Termes IGN] image proche infrarouge [Termes IGN] modèle orienté objet [Termes IGN] phytoécologie Résumé : (Auteur) Une cartographie inédite de la végétation de Bretagne sera accessible en totalité en ligne en décembre. Produite par télédétection grâce à une méthode semi-automatisée innovante, elle répond aux nouveaux besoins des acteurs de la biodiversité et de l'aménagement du territoire. Numéro de notice : A2020-707 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET/IMAGERIE Nature : Article nature-HAL : ArtSansCL DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96281 [article]

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Object-based classification of mixed forest types in Mongolia / E. Nyamjargal in Geocarto international, vol 35 n° 14 ([15/10/2020])  

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Ensemble learning for hyperspectral image classification using tangent collaborative representation / Hongjun Su in IEEE Transactions on geoscience and remote sensing, vol 58 n° 6 (June 2020)  

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Discrimination of different sea ice types from CryoSat-2 satellite data using an Object-based Random Forest (ORF) / Su Shu in Marine geodesy, Vol 43 n° 3 (May 2020)  

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Classification and segmentation of mining area objects in large-scale spares Lidar point cloud using a novel rotated density network / Yueguan Yan in ISPRS International journal of geo-information, vol 9 n° 3 (March 2020)  

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3D iterative spatiotemporal filtering for classification of multitemporal satellite data sets / Hessah Albanwan in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 1 (January 2020)  

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Analyse automatique du couvert végétal pour la gestion du risque végétation en milieu ferroviaire à partir d'imagerie aérienne / Hélène Rouillon (2020)  

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Classification of poplar trees with object-based ensemble learning algorithms using Sentinel-2A imagery / H. Tombul in Journal of geodetic science, vol 10 n° 1 (January 2020)  

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Identification of alpine glaciers in the central Himalayas using fully polarimetric L-Band SAR data / Guo-Hui Yao in IEEE Transactions on geoscience and remote sensing, vol 58 n° 1 (January 2020)  

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Satellite image time series classification with pixel-set encoders and temporal self-attention / Vivien Sainte Fare Garnot (2020)    

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Half a percent of labels is enough: efficient animal detection in UAV imagery using deep CNNs and active learning / Benjamin Kellenberger in IEEE Transactions on geoscience and remote sensing, vol 57 n° 12 (December 2019)  

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