Centre de documentation
scientifique

Investigating the impact of pan sharpening on the accuracy of land cover mapping in Landsat OLI imagery / Komeil Rokni in Geodesy and cartography, vol 49 n° 1 (January 2023)  

Public [article]  inGeodesy and cartography > vol 49 n° 1 (January 2023) . - pp 12 - 18 Titre : Investigating the impact of pan sharpening on the accuracy of land cover mapping in Landsat OLI imagery Type de document : Article/Communication Auteurs : Komeil Rokni, Auteur Année de publication : 2023 Article en page(s) : pp 12 - 18 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme de Gram-Schmidt [Termes IGN] carte d'occupation du sol [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] image Landsat-OLI [Termes IGN] image multibande [Termes IGN] image panchromatique [Termes IGN] Kappa de Cohen [Termes IGN] matrice de confusion [Termes IGN] pansharpening (fusion d'images) [Termes IGN] précision de la classification Résumé : (auteur) Pan Sharpening is normally applied to sharpen a multispectral image with low resolution by using a panchromatic image with a higher resolution, to generate a high resolution multispectral image. The present study aims at assessing the power of Pan Sharpening on improvement of the accuracy of image classification and land cover mapping in Landsat 8 OLI imagery. In this respect, different Pan Sharpening algorithms including Brovey, Gram-Schmidt, NNDiffuse, and Principal Components were applied to merge the Landsat OLI panchromatic band (15 m) with the Landsat OLI multispectral: visible and infrared bands (30 m), to generate a new multispectral image with a higher spatial resolution (15 m). Subsequently, the support vector machine approach was utilized to classify the original Landsat and resulting Pan Sharpened images to generate land cover maps of the study area. The outcomes were then compared through the generation of confusion matrix and calculation of kappa coefficient and overall accuracy. The results indicated superiority of NNDiffuse algorithm in Pan Sharpening and improvement of classification accuracy in Landsat OLI imagery, with an overall accuracy and kappa coefficient of about 98.66% and 0.98, respectively. Furthermore, the result showed that the Gram-Schmidt and Principal Components algorithms also slightly improved the accuracy of image classification compared to original Landsat image. The study concluded that image Pan Sharpening is useful to improve the accuracy of image classification in Landsat OLI imagery, depending on the Pan Sharpening algorithm used for this purpose. Numéro de notice : A2023-142 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3846/gac.2023.15308 Date de publication en ligne : 17/02/2023 En ligne : https://doi.org/10.3846/gac.2023.15308 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102712 [article]

Investigating the efficiency of deep learning methods in estimating GPS geodetic velocity / Omid Memarian Sorkhabi in Earth and space science, vol 9 n° 10 (October 2022)  

Public [article]  inEarth and space science > vol 9 n° 10 (October 2022) . - 8 p. Titre : Investigating the efficiency of deep learning methods in estimating GPS geodetic velocity Type de document : Article/Communication Auteurs : Omid Memarian Sorkhabi, Auteur ; Muhammed Milani, Auteur ; Seyed Mehdi Seyed Alizadeh, Auteur Année de publication : 2022 Article en page(s) : 8 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes IGN] apprentissage profond [Termes IGN] champ de vitesse [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] classification par réseau neuronal récurrent [Termes IGN] géodynamique [Termes IGN] point géodésique [Termes IGN] positionnement par GPS [Termes IGN] station GPS [Termes IGN] tectonique Résumé : (auteur) Geodetic velocity (GV) has many applications in tectonic motion determination and geodynamic studies. Due to the high cost of global navigation satellite system stations, deep learning methods have been investigated to estimate GV. In this research, four methods of convolutional neural networks (CNNs), deep Boltzmann machines, deep belief net and recurrent neural networks have been applied. The GV of 42 global positioning system stations is entered the deep learning methods. The outputs of the four methods have successfully passed the normality test. The results show that the CNN method has a lower goodness of fit and root mean square error (RMSE). CNN can learn different dependencies and extract features. Numéro de notice : A2022-757 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1029/2021EA002202 Date de publication en ligne : 22/09/2022 En ligne : https://doi.org/10.1029/2021EA002202 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101763 [article]

Adaptive block modeling of time dependent variations of datum reference points in a tectonically active area / Chun-Yun Chou in Survey review, vol 54 n° 386 (September 2022)  

Public [article]  inSurvey review > vol 54 n° 386 (September 2022) . - pp 404 - 419 Titre : Adaptive block modeling of time dependent variations of datum reference points in a tectonically active area Type de document : Article/Communication Auteurs : Chun-Yun Chou, Auteur ; Jen-Yu Han, Auteur Année de publication : 2022 Article en page(s) : pp 404 - 419 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes de référence et réseaux [Termes IGN] analyse de groupement [Termes IGN] angle d'Euler [Termes IGN] champ de vitesse [Termes IGN] Cinématique [Termes IGN] collocation par moindres carrés [Termes IGN] données GNSS [Termes IGN] formule d'Euler [Termes IGN] matrice de covariance [Termes IGN] rotation [Termes IGN] série temporelle [Termes IGN] station GNSS [Termes IGN] système de référence local [Termes IGN] Taïwan [Termes IGN] tectonique des plaques [Termes IGN] variation temporelle Résumé : (auteur) Although a dynamic or semi-dynamic datum has been adopted in some countries, it remains a challenge if a long-term stable datum is to be established in a tectonic active area. This study presents an approach to realistically reflect the time dependent behaviors of ground reference points while maintaining the long-term stability of a datum. An adaptive approach coupled with the Euler motion model is proposed for dividing an area into blocks. A least-squares collocation is then applied for modeling the residual velocities in each block. A case study using the data from 375 continuously operated GNSS stations in Taiwan is presented. It is illustrated that the complex surface kinematics in this region can be divided into three blocks. Significant reductions up to 64% of residual velocities were obtained. This shows that a stable datum can be established in a region with active and complicated surface kinematics by implementing the proposed. Numéro de notice : A2022-658 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2021.1949194 Date de publication en ligne : 12/07/2021 En ligne : https://doi.org/10.1080/00396265.2021.1949194 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101509 [article]

Evaluation of the GSRM2.1 and the NUVEL1-A values in Europe using SLR and VLBI based geodetic velocity fields / Mina Rahmani in Survey review, vol 54 n° 385 (July 2022)  

Public [article]  inSurvey review > vol 54 n° 385 (July 2022) . - pp 349 - 362 Titre : Evaluation of the GSRM2.1 and the NUVEL1-A values in Europe using SLR and VLBI based geodetic velocity fields Type de document : Article/Communication Auteurs : Mina Rahmani, Auteur ; Vahab Nafisi, Auteur ; Sigrid Böhm, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 349 - 362 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] analyse comparative [Termes IGN] champ de vitesse [Termes IGN] cohérence des données [Termes IGN] données ITGB [Termes IGN] données TLS (télémétrie) [Termes IGN] magnitude [Termes IGN] tectonique des plaques Résumé : (auteur) The NUVEL1-A is one of the old and popular plate tectonic models. While the NUVEL1-A is a geological-based model, recently a model has been proposed (GSRM2.1 model) which is based on the results of space geodetic techniques. In this work, we investigate the consistency of these models with the VLBI and SLR results in Europe. Direction and magnitude of the horizontal motion from NUVEL-1A and GSRM2.1 models are compared with corresponding values from both geodetic techniques. This comparison provides valuable deductions such as: (1) The values of geodetic-based model (GSRM2.1) show better agreement with SLR and VLBI results (2) In each comparison between geodetic results and modelled values, direction divergence is larger than magnitude difference. Numéro de notice : A2022-536 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2021.1943633 Date de publication en ligne : 25/06/2021 En ligne : https://doi.org/10.1080/00396265.2021.1943633 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101092 [article]

Detecting interchanges in road networks using a graph convolutional network approach / Min Yang in International journal of geographical information science IJGIS, vol 36 n° 6 (June 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 6 (June 2022) . - pp 1119 - 1139 Titre : Detecting interchanges in road networks using a graph convolutional network approach Type de document : Article/Communication Auteurs : Min Yang, Auteur ; Chenjun Jiang, Auteur ; Xiongfeng Yan, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1119 - 1139 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] analyse vectorielle [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] classification semi-dirigée [Termes IGN] détection d'objet [Termes IGN] échangeur routier [Termes IGN] extraction de traits caractéristiques [Termes IGN] modélisation [Termes IGN] noeud [Termes IGN] Pékin (Chine) [Termes IGN] réseau neuronal de graphes [Termes IGN] réseau routier [Termes IGN] Wuhan (Chine) Résumé : (auteur) Detecting interchanges in road networks benefit many applications, such as vehicle navigation and map generalization. Traditional approaches use manually defined rules based on geometric, topological, or both properties, and thus can present challenges for structurally complex interchange. To overcome this drawback, we propose a graph-based deep learning approach for interchange detection. First, we model the road network as a graph in which the nodes represent road segments, and the edges represent their connections. The proposed approach computes the shape measures and contextual properties of individual road segments for features characterizing the associated nodes in the graph. Next, a semi-supervised approach uses these features and limited labeled interchanges to train a graph convolutional network that classifies these road segments into an interchange and non-interchange segments. Finally, an adaptive clustering approach groups the detected interchange segments into interchanges. Our experiment with the road networks of Beijing and Wuhan achieved a classification accuracy >95% at a label rate of 10%. Moreover, the interchange detection precision and recall were 79.6 and 75.7% on the Beijing dataset and 80.6 and 74.8% on the Wuhan dataset, respectively, which were 18.3–36.1 and 17.4–19.4% higher than those of the existing approaches based on characteristic node clustering. Numéro de notice : A2022-404 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.2024195 Date de publication en ligne : 11/03/2022 En ligne : https://doi.org/10.1080/13658816.2021.2024195 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100716 [article]

Meta-learning based hyperspectral target detection using siamese network / Yulei Wang in IEEE Transactions on geoscience and remote sensing, vol 60 n° 4 (April 2022)  

Permalink

HV-LSC-ex2 : velocity field interpolation using extended least-squares collocation / Rebekka Steffen in Journal of geodesy, vol 96 n° 3 (March 2022)  

Permalink

Estimation of Lesser Antilles vertical velocity fields using a GNSS-PPP software comparison / Pierre Sakic-Kieffer (2022)  

Permalink

A spatial model of cognitive distance in cities / Ed Manley in International journal of geographical information science IJGIS, vol 35 n° 11 (November 2021)  

Permalink

A vector-based method for drainage network analysis based on LiDAR data / Fangzheng Lyu in Computers & geosciences, vol 156 (November 2021)  

Permalink

A novel method based on deep learning, GIS and geomatics software for building a 3D city model from VHR satellite stereo imagery / Massimiliano Pepe in ISPRS International journal of geo-information, vol 10 n° 10 (October 2021)  

Permalink

Machine learning and geodesy: A survey / Jemil Butt in Journal of applied geodesy, vol 15 n° 2 (April 2021)  

Permalink

Benefiting from local rigidity in 3D point cloud processing / Zan Gojcic (2021)  

Permalink

Change detection of land use and land cover, using landsat-8 and sentinel-2A images / Mohammed Abdulmohsen Alhedyan (2021)  

Permalink

Fusion of ground penetrating radar and laser scanning for infrastructure mapping / Dominik Merkle in Journal of applied geodesy, vol 15 n° 1 (January 2021)  

Permalink