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Evaluation of Landsat 8 image pansharpening in estimating soil organic matter using multiple linear regression and artificial neural networks / Abdelkrim Bouasria in Geo-spatial Information Science, vol 25 n° 3 (October 2022)
[article]
Titre : Evaluation of Landsat 8 image pansharpening in estimating soil organic matter using multiple linear regression and artificial neural networks Type de document : Article/Communication Auteurs : Abdelkrim Bouasria, Auteur ; Khalid Ibno Namra, Auteur ; Abdelmejid Rahimi, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 353 - 364 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] état du sol
[Termes IGN] image Landsat-OLI
[Termes IGN] image panchromatique
[Termes IGN] Maroc
[Termes IGN] matière organique
[Termes IGN] modèle de simulation
[Termes IGN] pansharpening (fusion d'images)
[Termes IGN] Perceptron multicouche
[Termes IGN] régression multiple
[Termes IGN] réseau neuronal artificielRésumé : (auteur) In agricultural systems, the regular monitoring of Soil Organic Matter (SOM) dynamics is essential. This task is costly and time-consuming when using the conventional method, especially in a very fragmented area and with intensive agricultural activity, such as the area of Sidi Bennour. The study area is located in the Doukkala irrigated perimeter in Morocco. Satellite data can provide an alternative and fill this gap at a low cost. Models to predict SOM from a satellite image, whether linear or nonlinear, have shown considerable interest. This study aims to compare SOM prediction using Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN). A total of 368 points were collected at a depth of 0–30 cm and analyzed in the laboratory. An image at 15 m resolution (MSPAN) was produced from a 30 m resolution (MS) Landsat-8 image using image pansharpening processing and panchromatic band (15 m). The results obtained show that the MLR models predicted the SOM with (training/validation) R2 values of 0.62/0.63 and 0.64/0.65 and RMSE values of 0.23/0.22 and 0.22/0.21 for the MS and MSPAN images, respectively. In contrast, the ANN models predicted SOM with R2 values of 0.65/0.66 and 0.69/0.71 and RMSE values of 0.22/0.10 and 0.21/0.18 for the MS and MSPAN images, respectively. Image pansharpening improved the prediction accuracy by 2.60% and 4.30% and reduced the estimation error by 0.80% and 1.30% for the MLR and ANN models, respectively. Numéro de notice : A2022-722 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/10095020.2022.2026743 Date de publication en ligne : 15/02/2022 En ligne : https://doi.org/10.1080/10095020.2022.2026743 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101665
in Geo-spatial Information Science > vol 25 n° 3 (October 2022) . - pp 353 - 364[article]GNSS best integer equivariant estimation combining with integer least squares estimation: an integrated ambiguity resolution method with optimal integer aperture test / Liye Ma in GPS solutions, vol 26 n° 4 (October 2022)
[article]
Titre : GNSS best integer equivariant estimation combining with integer least squares estimation: an integrated ambiguity resolution method with optimal integer aperture test Type de document : Article/Communication Auteurs : Liye Ma, Auteur ; Yidong Lou, Auteur ; Liguo Lu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 100 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes IGN] analyse comparative
[Termes IGN] méthode des moindres carrés
[Termes IGN] phase GNSS
[Termes IGN] positionnement par GNSS
[Termes IGN] précision du positionnement
[Termes IGN] résolution d'ambiguïtéRésumé : (auteur) Accurate and reliable carrier phase ambiguity resolution (AR) is the key to global navigation satellite system (GNSS) high-precision navigation and positioning applications. The integer least squares (ILS) estimation and the best integer equivariant (BIE) estimation are two widely used AR method, with the former considered to have the highest success rate and the latter to be optimal in the minimum mean squared error (MSE) sense. We analyzed three key issues of applying the BIE method in detail, including the use boundary of BIE, the number of candidates to be involved, and the weight determination among ambiguity candidates. It has been demonstrated that the BIE estimator is superior to ILS estimator from an overall perspective, but not always the best in each specific epoch. Therefore, we recommend constructing an integrated ambiguity resolution scheme that combines BIE with ILS, and we propose to adopt the optimal integer aperture (OIA) test as a criterion to distinguish the two. Moreover, a new criterion referred to the OIA test is proposed to determine the number of candidates involved in the BIE estimator. We also attempt to add the quadratic forms of baseline residuals into the weight function of BIE, aiming to reach a more accurate estimator. Finally, an integrated AR method that combines ILS with BIE and distinguished by the OIA test is proposed, named OIA-BIE. A set of real-measured vehicle data are tested to evaluate its performance, compared to least squares (LS), ILS, and the original BIE. The results show that the positioning accuracy of OIA-BIE is a little better than BIE, better than ILS, and significantly better than LS. Moreover, the average time consumption of ILS, BIE, and OIA-BIE are also evaluated, with 1.15, 14.62, and 3.71 ms, respectively, and OIA-BIE is four times more efficient than BIE. Numéro de notice : A2022-542 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1007/s10291-022-01285-5 Date de publication en ligne : 03/07/2022 En ligne : https://doi.org/10.1007/s10291-022-01285-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101107
in GPS solutions > vol 26 n° 4 (October 2022) . - n° 100[article]Identify urban building functions with multisource data: a case study in Guangzhou, China / Yingbin Deng in International journal of geographical information science IJGIS, vol 36 n° 10 (October 2022)
[article]
Titre : Identify urban building functions with multisource data: a case study in Guangzhou, China Type de document : Article/Communication Auteurs : Yingbin Deng, Auteur ; Renrong Chen, Auteur ; Yang Ji, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2060 - 2085 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] approche hiérarchique
[Termes IGN] batiment commercial
[Termes IGN] bâtiment industriel
[Termes IGN] bâtiment public
[Termes IGN] Canton (Kouangtoung)
[Termes IGN] données multisources
[Termes IGN] empreinte
[Termes IGN] exploration de données
[Termes IGN] Extreme Gradient Machine
[Termes IGN] figure géométrique
[Termes IGN] image Gaofen
[Termes IGN] logement
[Termes IGN] point d'intérêt
[Termes IGN] zone urbaineRésumé : (auteur) Building function type is an important parameter for urban planning and disaster management. However, existing identification methods do not always correctly recognize all building functions because of missing point of interest (POI) data in private areas. In this study, we proposed a hierarchical data-mining model to identify building function types using accessible auxiliary data, which was then applied to a case study. Residential building property was assessed to address missing residential POIs. The building functions were assigned to one of five different types, or a mixed-function type. Standard deviation and mean values extracted from remotely sensed images, distances to major roads, and building shape parameters were used to infer the function types of buildings without assigned function types. The proposed model was able to identify 65% of buildings not previously assigned as residential through the POI, with an overall accuracy of 87%. In addition, all buildings were successfully assigned a function type of residential, commercial, office, warehouse, public service, or mixed-function, with an overall accuracy of 85% for unclassified buildings. Our results demonstrated that missing POI data in private areas could be addressed by integration with multisource data using a simple method. Numéro de notice : A2022-739 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2046756 Date de publication en ligne : 07/03/2022 En ligne : https://doi.org/10.1080/13658816.2022.2046756 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101716
in International journal of geographical information science IJGIS > vol 36 n° 10 (October 2022) . - pp 2060 - 2085[article]Incremental road network update method with trajectory data and UAV remote sensing imagery / Jianxin Qin in ISPRS International journal of geo-information, vol 11 n° 10 (October 2022)
[article]
Titre : Incremental road network update method with trajectory data and UAV remote sensing imagery Type de document : Article/Communication Auteurs : Jianxin Qin, Auteur ; Wenjie Yang, Auteur ; Tao Wu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 502 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage profond
[Termes IGN] données spatiotemporelles
[Termes IGN] extraction du réseau routier
[Termes IGN] image captée par drone
[Termes IGN] mise à jour de base de données
[Termes IGN] modèle de Markov caché
[Termes IGN] OpenStreetMap
[Termes IGN] réseau routier
[Termes IGN] segmentation
[Termes IGN] trace au solRésumé : (auteur) GPS trajectory and remote sensing data are crucial for updating urban road networks because they contain critical spatial and temporal information. Existing road network updating methods, whether trajectory-based (TB) or image-based (IB), do not integrate the characteristics of both types of data. This paper proposed and implemented an incremental update method for rapid road network checking and updating. A composite update framework for road networks is established, which integrates trajectory data and UAV remote sensing imagery. The research proposed utilizing connectivity between adjacent matched points to solve the problem of updating problematic road segments in networks based on the features of the Hidden Markov Model (HMM) map-matching method in identifying new road segments. Deep learning is used to update the local road network in conjunction with the flexible and high-precision characteristics of UAV remote sensing. Additionally, the proposed method is evaluated against two baseline methods through extensive experiments based on real-world trajectories and UAV remote sensing imagery. The results show that our method has higher extraction accuracy than the TB method and faster updates than the IB method. Numéro de notice : A2022-791 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/ijgi11100502 Date de publication en ligne : 27/09/2022 En ligne : https://doi.org/10.3390/ijgi11100502 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101904
in ISPRS International journal of geo-information > vol 11 n° 10 (October 2022) . - n° 502[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)
[article]
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] tectoniqueRésumé : (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
in Earth and space science > vol 9 n° 10 (October 2022) . - 8 p.[article]Investigation of recognition and classification of forest fires based on fusion color and textural features of images / Cong Li in Forests, vol 13 n° 10 (October 2022)PermalinkMachine learning and natural language processing of social media data for event detection in smart cities / Andrei Hodorog in Sustainable Cities and Society, vol 85 (October 2022)PermalinkModelling and prediction of GNSS time series using GBDT, LSTM and SVM machine learning approaches / Wenzong Gao in Journal of geodesy, vol 96 n° 10 (October 2022)PermalinkMonitoring spatiotemporal soil moisture changes in the subsurface of forest sites using electrical resistivity tomography (ERT) / Julian Fäth in Journal of Forestry Research, vol 33 n° 5 (October 2022)PermalinkMulti‑constellation GNSS interferometric reflectometry for the correction of long-term snow height retrieval on sloping topography / Wei Zhou in GPS solutions, vol 26 n° 4 (October 2022)PermalinkMultisource forest inventories: A model-based approach using k-NN to reconcile forest attributes statistics and map products / Ankit Sagar in ISPRS Journal of photogrammetry and remote sensing, vol 192 (October 2022)PermalinkNovel algorithm based on geometric characteristics for tree branch skeleton extraction from LiDAR point cloud / Jie Yang in Forests, vol 13 n° 10 (October 2022)PermalinkPredicting the variability in pedestrian travel rates and times using crowdsourced GPS data / Michael J. Campbell in Computers, Environment and Urban Systems, vol 97 (October 2022)PermalinkA relation-augmented embedded graph attention network for remote sensing object detection / Shu Tian in IEEE Transactions on geoscience and remote sensing, vol 60 n° 10 (October 2022)PermalinkSemi-supervised adversarial recognition of refined window structures for inverse procedural façade modelling / Han Hu in ISPRS Journal of photogrammetry and remote sensing, vol 192 (October 2022)Permalink