Descripteur
Termes IGN > imagerie
imagerie
Commentaire :
Terme regroupant photographies et images issues de différents capteurs.
|
Documents disponibles dans cette catégorie (8192)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
Evaluating Sentinel-1A datasets for rice leaf area index estimation based on machine learning regression models / Lamin R. Mansaray in Geocarto international, vol 37 n° 5 ([01/03/2022])
[article]
Titre : Evaluating Sentinel-1A datasets for rice leaf area index estimation based on machine learning regression models Type de document : Article/Communication Auteurs : Lamin R. Mansaray, Auteur ; Fumin Wang, Auteur ; Adam Sheka Kanu, Auteur ; Lingbo Yang, Auteur Année de publication : 2022 Article en page(s) : pp 1225 - 1236 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] apprentissage automatique
[Termes IGN] Chine
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] Extreme Gradient Machine
[Termes IGN] image Sentinel-SAR
[Termes IGN] jeu de données localisées
[Termes IGN] Leaf Area Index
[Termes IGN] modèle de régression
[Termes IGN] plus proche voisin, algorithme du
[Termes IGN] polarisation
[Termes IGN] rizièreRésumé : (Auteur) Three Sentinel-1A datasets in vertical transmitted and horizontal received (VH) and vertical transmitted and vertical received (VV) polarisations, and the linear combination of VH and VV (VHVV) are evaluated for rice green leaf area index (LAI) estimation using four machine learning regression models [Support Vector Machine (SVM), k-Nearest Neighbour (k-NN), Random Forest (RF) and Gradient Boosting Decision Tree (GBDT)]. Results showed that for the entire growing season, VV outperformed VH, recording an R2 of 0.68 and an RMSE of 0.98 m2/m2 with the k-NN model. However, VHVV produced the most accurate estimates with GBDT (R2 of 0.82 and RMSE of 0.68 m2/m2), followed by that of VHVV with RF (R2 of 0.78 and RMSE of 0.90 m2/m2). Our findings have further confirmed that combining VH and VV data can achieve improved rice growth modelling, and that tree-based algorithms can better handle data dimensionality. Numéro de notice : A2022-274 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1773545 Date de publication en ligne : 05/06/2020 En ligne : https://doi.org/10.1080/10106049.2020.1773545 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100753
in Geocarto international > vol 37 n° 5 [01/03/2022] . - pp 1225 - 1236[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 059-2022051 RAB Revue Centre de documentation En réserve L003 Disponible Evaluating the 3D integrity of underwater structure from motion workflows / Ian M. Lochhead in Photogrammetric record, vol 37 n° 177 (March 2022)
[article]
Titre : Evaluating the 3D integrity of underwater structure from motion workflows Type de document : Article/Communication Auteurs : Ian M. Lochhead, Auteur Année de publication : 2022 Article en page(s) : pp 35 - 60 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] auscultation d'ouvrage
[Termes IGN] chaîne de traitement
[Termes IGN] étalonnage d'instrument
[Termes IGN] fond marin
[Termes IGN] image sous-marine
[Termes IGN] modélisation 3D
[Termes IGN] Pacifique nord
[Termes IGN] récif corallien
[Termes IGN] semis de points
[Termes IGN] semis de points clairsemés
[Termes IGN] structure-from-motionRésumé : (auteur) Structure from motion (SfM) is an accessible and non-intrusive method of three-dimensional (3D) data capture popular for tropical coral reef surveying. In the north-east Pacific Ocean, where there are many environmentally sensitive benthic organisms whose morphology and function are equally important, SfM surveys are less commonly studied. Temperate waters pose unique challenges to SfM workflows, which must be systematically unpacked to understand their impact on data quality and veracity. This uncertainty raises broader questions concerning SfM as a spatial data-acquisition and ecological characterisation method in temperate waters, and whether a systematic workflow assessment reveals vital relationships between SfM implementation parameters, 3D data products and their implications for underwater SfM surveys. This paper, the second of two empirical assessments, reports on a series of wet-lab and dryland tests quantifying the impact that temperate waters, underwater cameras, and photograph quantity and configuration have on SfM accuracy. These tests provided crucial accuracy benchmarks informing subsequent field-based surveys and revealed that underwater SfM workflows can generate highly accurate 3D models in temperate waters. Numéro de notice : A2022-253 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : doi.org/10.1111/phor.12399 Date de publication en ligne : 07/03/2022 En ligne : https://doi.org/10.1111/phor.12399 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100216
in Photogrammetric record > vol 37 n° 177 (March 2022) . - pp 35 - 60[article]Exploring the relationship between the 2D/3D architectural morphology and urban land surface temperature based on a boosted regression tree: A case study of Beijing, China / Zhen Li in Sustainable Cities and Society, vol 78 (March 2022)
[article]
Titre : Exploring the relationship between the 2D/3D architectural morphology and urban land surface temperature based on a boosted regression tree: A case study of Beijing, China Type de document : Article/Communication Auteurs : Zhen Li, Auteur ; Dan Hu, Auteur Année de publication : 2022 Article en page(s) : n° 103392 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] Bâti-3D
[Termes IGN] classification et arbre de régression
[Termes IGN] corrélation
[Termes IGN] données localisées 2D
[Termes IGN] hauteur du bâti
[Termes IGN] ilot thermique urbain
[Termes IGN] image Landsat-OLI
[Termes IGN] milieu urbain
[Termes IGN] morphologie urbaine
[Termes IGN] Pékin (Chine)
[Termes IGN] saison
[Termes IGN] température au solRésumé : (auteur) With rapid urbanization, urban three-dimensional morphology and its ecological effects have received more attention. However, thorough investigations into the multiple scale impact of the 2D/3D architectural morphology on urban land surface temperature (LST) remain limited. Taking Beijing as a case study area, we quantified the contributions of the 2D/3D architectural morphology indicators and revealed their marginal effects on multiple scales using the boosted regression trees (BRT) method. The results showed that (1) the building coverage ratio and building height were the most significant factors influencing the LST across all spatial scales and seasons, (2) the 3D shape index, 3D fractal, and 3D adjacency were found to be influential factors, with sum contributions varying from 6.0% to 37.7%, and (3) in summer, the 3D shape index showed a stepwise negative correlation with the LST. The 3D fractal and 3D adjacency exhibited both positive and negative correlations with the LST. When the spatial scale was 240 m, the regulation amplitudes for the 3D shape index, 3D fractal, and 3D adjacency were 2.0°C, 1.0°C and 1.0°C, respectively. These findings provide quantitative insights that can be used to improve urban thermal environments and achieve sustainable urban development by adjusting architectural morphology. Numéro de notice : A2022-242 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.scs.2021.103392 Date de publication en ligne : 28/12/2021 En ligne : https://doi.org/10.1016/j.scs.2021.103392 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100169
in Sustainable Cities and Society > vol 78 (March 2022) . - n° 103392[article]Extraction from high-resolution remote sensing images based on multi-scale segmentation and case-based reasoning / Jun Xu in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 3 (March 2022)
[article]
Titre : Extraction from high-resolution remote sensing images based on multi-scale segmentation and case-based reasoning Type de document : Article/Communication Auteurs : Jun Xu, Auteur ; Jiasong Li, Auteur ; Hao Peng, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 199 - 205 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] analyse d'image orientée objet
[Termes IGN] classification barycentrique
[Termes IGN] distance de Kullback-Leibler
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image à haute résolution
[Termes IGN] image Worldview
[Termes IGN] masque
[Termes IGN] occupation du sol
[Termes IGN] segmentation d'image
[Termes IGN] segmentation multi-échelle
[Termes IGN] séparateur à vaste margeRésumé : (auteur) In object-oriented information extraction from high-resolution remote sensing images, the segmentation and classification of images involves considerable manual participation, which limits the development of automation and intelligence for these purposes. Based on the multi-scale segmentation strategy and case-based reasoning, a new method for extracting high-resolution remote sensing image information by fully using the image and nonimage features of the case object is proposed. Feature selection and weight learning are used to construct a multi-level and multi-layer case library model of surface cover classification reasoning. Combined with image mask technology, this method is applied to extract surface cover classification information from remote sensing images using different sensors, time, and regions. Finally, through evaluation of the extraction and recognition rates, the accuracy and effectiveness of this method was verified. Numéro de notice : A2022-202 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.20-00104R3 Date de publication en ligne : 01/03/2022 En ligne : https://doi.org/10.14358/PERS.20-00104R3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100006
in Photogrammetric Engineering & Remote Sensing, PERS > vol 88 n° 3 (March 2022) . - pp 199 - 205[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 105-2022031 SL Revue Centre de documentation Revues en salle Disponible Feasibility of mapping radioactive minerals in high background radiation areas using remote sensing techniques / J.O. Ondieki in International journal of applied Earth observation and geoinformation, vol 107 (March 2022)
[article]
Titre : Feasibility of mapping radioactive minerals in high background radiation areas using remote sensing techniques Type de document : Article/Communication Auteurs : J.O. Ondieki, Auteur ; C.O. Mito, Auteur ; M.I. Kaniu, Auteur Année de publication : 2022 Article en page(s) : n° 102700 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse de groupement
[Termes IGN] carte thématique
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] données géologiques
[Termes IGN] image Landsat-OLI
[Termes IGN] Kappa de Cohen
[Termes IGN] Kenya
[Termes IGN] minerai
[Termes IGN] pollution radioactive
[Termes IGN] précision de la classification
[Termes IGN] radioactivité
[Termes IGN] signature spectraleRésumé : (auteur) This study investigates the utility of using remote sensing and geographic information system techniques to accurately infer the presence of radioactive minerals in a typical high background radiation area (HBRA) by analyzing spectral signatures of associated soil, rocks and vegetation. To accomplish this, both unsupervised (K-Means Clustering) and supervised classification techniques based on a maximum likelihood classifier (MLC) were applied to Landsat-8 Imager data from Mrima Hill on Kenya's south coast. The hill is surrounded by dense tropical forest and deeply weathered soils which are rich in Nb, Th, and rare earth elements. Due to high activity concentrations of 232Th (>8 times higher than the world average value for soil), the hill has been designated as a geogenic HBRA. Based on the underlying geological formations, four classifications of vegetation and two classifications of soil/rocks were established and used to indicate the presence of radioactive minerals in the area. Measurements of air-absorbed gamma dose-rates in the area were successfully used to validate these findings. The application of the MLC method on Landsat satellite data shows that this method can be used as a powerful tool to explore and improve radioactive minerals mapping in HBRAs, the overall classification accuracy of Landsat8 OLI data using botanical technique is 80% and the Kappa Coefficient is 0.6. The overall classification accuracy using soil/rocks spectral signatures is 91% and the Kappa Coefficient is 0.7. Finally, the study demonstrated the general utility of remote sensing techniques in radioactive mineral surveys as well as environmental radiological assessments, particularly in resource-constrained settings. Numéro de notice : A2022-194 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.jag.2022.102700 Date de publication en ligne : 02/02/2022 En ligne : https://doi.org/10.1016/j.jag.2022.102700 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99956
in International journal of applied Earth observation and geoinformation > vol 107 (March 2022) . - n° 102700[article]Flood monitoring by integration of remote sensing technique and multi-criteria decision making method / Hadi Farhadi in Computers & geosciences, vol 160 (March 2022)PermalinkLand surface phenology retrieval through spectral and angular harmonization of Landsat-8, Sentinel-2 and Gaofen-1 data / Jun Lu in Remote sensing, vol 14 n° 5 (March-1 2022)PermalinkLiDAR-based method for analysing landmark visibility to pedestrians in cities: case study in Kraków, Poland / Krystian Pyka in International journal of geographical information science IJGIS, vol 36 n° 3 (March 2022)PermalinkMonitoring coastal vulnerability by using DEMs based on UAV spatial data / Antonio Minervino Amodio in ISPRS International journal of geo-information, vol 11 n° 3 (March 2022)PermalinkMonitoring of phenological stage and yield estimation of sunflower plant using Sentinel-2 satellite images / Omer Gokberk Narin in Geocarto international, vol 37 n° 5 ([01/03/2022])PermalinkA novel regression method for harmonic analysis of time series / Qiang Zhou in ISPRS Journal of photogrammetry and remote sensing, vol 185 (March 2022)PermalinkProbabilistic unsupervised classification for large-scale analysis of spectral imaging data / Emmanuel Paradis in International journal of applied Earth observation and geoinformation, vol 107 (March 2022)PermalinkSimultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3 / Nima Pahlevan in Remote sensing of environment, vol 270 (March 2022)PermalinkTraffic sign three-dimensional reconstruction based on point clouds and panoramic images / Minye Wang in Photogrammetric record, vol 37 n° 177 (March 2022)PermalinkUltrahigh-resolution boreal forest canopy mapping: Combining UAV imagery and photogrammetric point clouds in a deep-learning-based approach / Linyuan Li in International journal of applied Earth observation and geoinformation, vol 107 (March 2022)PermalinkUsing street view images to identify road noise barriers with ensemble classification model and geospatial analysis / Kai Zhang in Sustainable Cities and Society, vol 78 (March 2022)PermalinkAboveground biomass estimation of an agro-pastoral ecology in semi-arid Bundelkhand region of India from Landsat data: a comparison of support vector machine and traditional regression models / Dibyendu Deb in Geocarto international, vol 37 n° 4 ([15/02/2022])PermalinkComparing methods to extract crop height and estimate crop coefficient from UAV imagery using structure from motion / Nitzan Malachy in Remote sensing, vol 14 n° 4 (February-2 2022)PermalinkMulti-species individual tree segmentation and identification based on improved mask R-CNN and UAV imagery in mixed forests / Chong Zhang in Remote sensing, vol 14 n° 4 (February-2 2022)PermalinkA national fuel type mapping method improvement using sentinel-2 satellite data / Alexandra Stefanidou in Geocarto international, vol 37 n° 4 ([15/02/2022])PermalinkSimulation of future forest and land use/cover changes (2019–2039) using the cellular automata-Markov model / Hasan Aksoy in Geocarto international, vol 37 n° 4 ([15/02/2022])PermalinkAn open science and open data approach for the statistically robust estimation of forest disturbance areas / Saverio Francini in International journal of applied Earth observation and geoinformation, vol 106 (February 2022)PermalinkBuilding 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])PermalinkA combination of convolutional and graph neural networks for regularized road surface extraction / Jingjing Yan in IEEE Transactions on geoscience and remote sensing, vol 60 n° 2 (February 2022)PermalinkDecision fusion of deep learning and shallow learning for marine oil spill detection / Junfang Yang in Remote sensing, vol 14 n° 3 (February-1 2022)Permalink