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Multiresolution analysis pansharpening based on variation factor for multispectral and panchromatic images from different times / Peng Wang in IEEE Transactions on geoscience and remote sensing, vol 61 n° 3 (March 2023)
[article]
Titre : Multiresolution analysis pansharpening based on variation factor for multispectral and panchromatic images from different times Type de document : Article/Communication Auteurs : Peng Wang, Auteur ; Hongyu Yao, Auteur ; Bo Huang, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 5401217 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse multirésolution
[Termes IGN] données multitemporelles
[Termes IGN] image multibande
[Termes IGN] image panchromatique
[Termes IGN] pansharpening (fusion d'images)
[Termes IGN] pouvoir de résolution géométriqueRésumé : (auteur) Most pansharpening methods refer to the fusion of the original low-resolution multispectral (MS) and high-resolution panchromatic (PAN) images acquired simultaneously over the same area. Due to its good robustness, multiresolution analysis (MRA) has become one of the important categories of pansharpening methods. However, when only MS and PAN images acquired at different times can be provided, the fusion results from current MRA methods are often not ideal due to the failure to effectively analyze multitemporal misalignments between MS and PAN images from different times. To solve this issue, MRA pansharpening based on variation factor for MS and PAN images from different times is proposed. The MRA pansharpening based on dual-scale regression model is first established, and the variation factor is then introduced to effectively analyze the multitemporal misalignments by using the alternating direction method of multipliers (ADMM), yielding the final fusion results. Experiments with synthetic and real datasets show that the proposed method exhibits significant performance improvement compared to the traditional pansharpening methods, as well as the state-of-the-art MRA methods. Visual comparisons demonstrate that the variation factor introduces encouraging improvements in the compensation of multitemporal misalignments in ground objects and advances pansharpening applications for MS and PAN images acquired at different times. Numéro de notice : A2023-184 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2023.3252001 En ligne : https://doi.org/10.1109/TGRS.2023.3252001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102956
in IEEE Transactions on geoscience and remote sensing > vol 61 n° 3 (March 2023) . - n° 5401217[article]MTMGNN: Multi-time multi-graph neural network for metro passenger flow prediction / Du Yin in Geoinformatica, vol 27 n° 1 (January 2023)
[article]
Titre : MTMGNN: Multi-time multi-graph neural network for metro passenger flow prediction Type de document : Article/Communication Auteurs : Du Yin, Auteur ; Renhe Jiang, Auteur ; Jiewen Deng, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 77 - 105 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] déformation temporelle dynamique (algorithme)
[Termes IGN] données multitemporelles
[Termes IGN] données spatiotemporelles
[Termes IGN] flux
[Termes IGN] gestion de trafic
[Termes IGN] origine - destination
[Termes IGN] réseau neuronal de graphes
[Termes IGN] système de transport intelligent
[Termes IGN] trafic urbain
[Termes IGN] transport public
[Termes IGN] utilisateurRésumé : (auteur) The passenger flow prediction of the public metro system is a core and critical part of the intelligent transportation system, and is essential for traffic management, metro planning, and emergency safety measures. Most methods chose the recent segment from historical data as input to predict the future traffic flow; however, this would lead to the loss of the inherent characteristic information of the metro passenger flow’s daily morning and evening peak. Therefore, this study aggregates the recent-term and long-term information and use a long-term Gated Convolutional Neural Network (Gated CNN) to extract the temporal feature from the complex historical data. On the other hand, typical models did not consider the different spatial dependencies between different metro stations; this work proposes various adjacent relationships to characterize the degree of association between nodes. In order to extract spatial and temporal features at the same time, the historical data of recent-term and long-term is merged together to extract spatial features through a multi-graph neural network module. By combining Gated CNN and multi-graph module, we propose a multi-time multi-graph neural network named MTMGNN for metro passenger flow prediction. The result of our experiment on real-world datasets shows that our model MTMGNN is better than all state-of-art methods. Numéro de notice : A2023-113 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-022-00466-1 Date de publication en ligne : 25/04/2022 En ligne : https://doi.org/10.1007/s10707-022-00466-1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102478
in Geoinformatica > vol 27 n° 1 (January 2023) . - pp 77 - 105[article]Decadal surface changes and displacements in Switzerland / Valentin Tertius Bickel in Journal of Geovisualization and Spatial Analysis, vol 6 n° 2 (December 2022)
[article]
Titre : Decadal surface changes and displacements in Switzerland Type de document : Article/Communication Auteurs : Valentin Tertius Bickel, Auteur ; Andrea Manconi, Auteur Année de publication : 2022 Article en page(s) : n° 24 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] corrélation d'images
[Termes IGN] détection de changement
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] données multitemporelles
[Termes IGN] effondrement de terrain
[Termes IGN] géomorphologie locale
[Termes IGN] glacier
[Termes IGN] Liechtenstein
[Termes IGN] modèle numérique de terrain
[Termes IGN] stéréophotogrammétrie
[Termes IGN] SuisseRésumé : (auteur) Multi-temporal, high-resolution, and homogeneous geospatial datasets acquired by space- and/or airborne sensors provide unprecedented opportunities for the characterization and monitoring of surface changes on very large spatial scales. Here, we demonstrate how an off-the-shelf, open-source image correlation algorithm can be combined with SwissALTI3D LiDAR-derived elevation data from different tracking periods to create country-scale surface displacement and vertical change maps of Switzerland, including Liechtenstein, with minimal computational effort. The results show that glacier displacement and ablation make up the most significant fraction of the detected surface changes in the last two decades. In addition, we identify numerous landslides and other geomorphic features, as well as manmade changes such as construction sites and landfills. All produced maps and data products are available online, free of charge. Numéro de notice : A2022-832 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s41651-022-00119-9 Date de publication en ligne : 01/08/2022 En ligne : https://doi.org/10.1007/s41651-022-00119-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102019
in Journal of Geovisualization and Spatial Analysis > vol 6 n° 2 (December 2022) . - n° 24[article]Exploring the influencing factors in identifying soil texture classes using multitemporal Landsat-8 and Sentinel-2 data / Yanan Zhou in Remote sensing, vol 14 n° 21 (November-1 2022)
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Titre : Exploring the influencing factors in identifying soil texture classes using multitemporal Landsat-8 and Sentinel-2 data Type de document : Article/Communication Auteurs : Yanan Zhou, Auteur ; Wei Wu, Auteur ; Hongbin Liu, Auteur Année de publication : 2022 Article en page(s) : n° 5571 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] composition des sols
[Termes IGN] données multitemporelles
[Termes IGN] Extreme Gradient Machine
[Termes IGN] Fleuve bleu (Chine)
[Termes IGN] image Landsat-8
[Termes IGN] image Sentinel-MSI
[Termes IGN] limon
[Termes IGN] qualité du sol
[Termes IGN] réflectance spectrale
[Termes IGN] texture du solRésumé : (auteur) Soil texture is a key soil property driving physical, chemical, biological, and hydrological processes in soils. The rapid development of remote sensing techniques shows great potential for mapping soil properties. This study highlights the effectiveness of multitemporal remote sensing data in identifying soil textural class by using retrieved vegetation properties as proxies of soil properties. The impacts of sensors, modeling resolutions, and modeling techniques on the accuracy of soil texture classification were explored. Multitemporal Landsat-8 and Sentinel-2 images were individually acquired at the same time periods. Three satellite-based experiments with different inputs, i.e., Landsat-8 data, Sentinel-2 data (excluding red-edge parameters), and Sentinel-2 data (including red-edge parameters) were conducted. Modeling was carried out at three spatial resolutions (10, 30, 60 m) using five machine-learning (ML) methods: random forest, support vector machine, gradient-boosting decision tree, categorical boosting, and super learner that combined the four former classifiers based on the stacking concept. In addition, a novel SHapley Addictive Explanation (SHAP) technique was introduced to explain the outputs of the ML model. The results showed that the sensors, modeling resolutions, and modeling techniques significantly affected the prediction accuracy. The models using Sentinel-2 data with red-edge parameters performed consistently best. The models usually gave better results at fine (10 m) and medium (30 m) modeling resolutions than at a coarse (60 m) resolution. The super learner provided higher accuracies than other modeling techniques and gave the highest values of overall accuracy (0.8429), kappa (0.7611), precision (0.8378), recall rate (0.8393), and F1-score (0.8398) at 30 m with Sentinel-2 data involving red-edge parameters. The SHAP technique quantified the contribution of each variable for different soil textural classes, revealing the critical roles of red-edge parameters in separating loamy soils. This study provides comprehensive insights into the effective modeling of soil properties on various scales using multitemporal optical images. Numéro de notice : A2022-856 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14215571 Date de publication en ligne : 04/11/2022 En ligne : https://doi.org/10.3390/rs14215571 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102104
in Remote sensing > vol 14 n° 21 (November-1 2022) . - n° 5571[article]Incorporation of digital elevation model, normalized difference vegetation index, and Landsat-8 data for land use land cover mapping / Jwan Al-Doski in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 8 (August 2022)
[article]
Titre : Incorporation of digital elevation model, normalized difference vegetation index, and Landsat-8 data for land use land cover mapping Type de document : Article/Communication Auteurs : Jwan Al-Doski, Auteur ; Faez M. Hassan, Auteur ; Hussein Abdelwahab Mossa, Auteur ; Aus A. Najim, Auteur Année de publication : 2022 Article en page(s) : pp 507 - 516 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] carte d'occupation du sol
[Termes IGN] carte d'utilisation du sol
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] données auxiliaires
[Termes IGN] image Landsat-8
[Termes IGN] Malaisie
[Termes IGN] MNS ASTER
[Termes IGN] modèle numérique de surface
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] ombre
[Termes IGN] précision de la classificationRésumé : (Auteur) Ancillary data are crucial in land use land cover (LULC) mapping process. This study goal is to investigate if adding Normalized Difference Vegetation Index (NDVI) and digital elevation model (DEM) data as ancillary data to the Landsat-8 spectral imagery (acquired on 14 April 2016) in the support vector machine (SVM ) classification process improves LULC mapping accuracy in GuaMusang, Malaysia. ENVI software was used to preprocess a single Landsat-8 image, convert it to reflectance, and calculate NDVI. ASTER-GDEM data were used to generate the DEM. The logical channel method was used to combine NDVI and DEM with Landsat-8 bands and limit the impact of shadows during SVM classification. The SVM accuracy was tested and evaluated on ancillary data and Landsat-8 spectral-based collection. The results revealed that the user's accuracy and producer's accuracy improved by 15.1% and 2.1%, for primary forest and by 17.93% and 28.86% for secondary forest, respectively. The classification reliability of the majority of LULC categories has increased significantly. Compared to SVM spectral-based set, the overall accuracy and kappa coefficient of the SVM ancillary-based set improved by 8.77% and 0.12, respectively. In conclusion, this article demonstrated that integrating DEM and NDVI data improves Landsat-8 image classification precision. Numéro de notice : A2022-805 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.21-00082R2 Date de publication en ligne : 01/08/2022 En ligne : https://doi.org/10.14358/PERS.21-00082R2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102132
in Photogrammetric Engineering & Remote Sensing, PERS > vol 88 n° 8 (August 2022) . - pp 507 - 516[article]Exemplaires(1)
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