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A quantitative comparison of regionalization methods / Orhun Aydun in International journal of geographical information science IJGIS, vol 35 n° 11 (November 2021)
[article]
Titre : A quantitative comparison of regionalization methods Type de document : Article/Communication Auteurs : Orhun Aydun, Auteur ; Mark V. Janikas, Auteur ; Renato Martins Assuncao, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 2287 - 2315 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse de groupement
[Termes IGN] données localisées
[Termes IGN] écorégion
[Termes IGN] exploration de données
[Termes IGN] partition d'image
[Termes IGN] partitionnement
[Termes IGN] segmentation en régionsRésumé : (auteur) Regionalization is the task of partitioning a set of contiguous areas into spatial clusters or regions. The theoretical and empirical literature focusing on regionalization is extensive, yet few quantitative comparisons have been conducted. We present a simulation study and explore the quality of frequently used and state-of-the-art regionalization algorithms, namely AZP, AZP-SA, AZPTabu, ARISEL, REDCAP, and SKATER, where the number of regions is an exogenous variable. The simulated benchmark data set consists of model realizations that represent various complexities in spatial data. Model families are defined with respect to regions’ shapes, value-mixing between regions, and the number of underlying spatial clusters. We evaluate the performance of different regionalization methods for realizations families using internal and external measures of regionalization quality. A large number of regionalization quality metrics expose a detailed profile of the analyzed methods’ strengths and weaknesses. We investigate the computational efficiency of every method as a function of the number of spatial units studied. We summarize results for different region families and discuss circumstances that make a certain method more desirable. We illustrate different regionalization algorithms’ implications on defining ecological regions for the conterminous US and compare them against expert-defined ecoregions. Numéro de notice : A2021-760 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1905819 Date de publication en ligne : 05/04/2021 En ligne : https://doi.org/10.1080/13658816.2021.1905819 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98789
in International journal of geographical information science IJGIS > vol 35 n° 11 (November 2021) . - pp 2287 - 2315[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2021111 SL Revue Centre de documentation Revues en salle Disponible Quels besoins de connaissances pour le futur des forêts en France ? Au-delà du plan de relance / Maya Leroy in Revue forestière française, vol 73 n° 1 (2021)
[article]
Titre : Quels besoins de connaissances pour le futur des forêts en France ? Au-delà du plan de relance Type de document : Article/Communication Auteurs : Maya Leroy, Auteur ; Jean-Daniel Bontemps , Auteur ; Elodie Brahic, Auteur ; Jean-Luc Dupouey, Auteur ; Pierre-Michel Forget, Auteur ; Serge Garcia, Auteur ; Valéry Gond, Auteur ; Andreas Nikolaus Kleinschmit von Lengefeld, Auteur ; Guy Landmann, Auteur ; Xavier Morin, Auteur ; Raphaël Pélissier, Auteur ; Nicolas Picard, Auteur ; Pascal Marty, Auteur Année de publication : 2021 Projets : 1-Pas de projet / Article en page(s) : pp 7 - 19 Note générale : bibliographie Langues : Français (fre) Descripteur : [Termes IGN] acquisition de connaissances
[Termes IGN] acteurs de la filière bois-forêt
[Termes IGN] changement climatique
[Termes IGN] conservation des ressources forestières
[Termes IGN] dynamique de la végétation
[Termes IGN] enjeu
[Termes IGN] forêt
[Termes IGN] France (administrative)
[Termes IGN] protection de la biodiversité
[Termes IGN] service écosystémique
[Termes IGN] territoire
[Vedettes matières IGN] ForesterieRésumé : (auteur) Le plan France Relance lancé en septembre 2020 prévoit des mesures forestières sur 2 ans, avec un accent sur la reconstitution des peuplements forestiers sinistrés, affaiblis par les sécheresses ou attaqués par les scolytes. Cependant la crise forestière liée au changement climatique est partie pour durer et les efforts sur les connaissances à acquérir pour aider la forêt à s’adapter au changement climatique devront être poursuivis sur le long terme. Nous identifions quatre enjeux principaux, fortement liés à la préservation de la biodiversité : 1) S’assurer des conditions de succès d’établissement des forêts plantées. 2) Tirer parti des dynamiques naturelles et de la biodiversité pour limiter les risques. 3) Raisonner territorialement, impliquer davantage les acteurs. 4) Connecter les enjeux nationaux aux enjeux économiques mondiaux. Numéro de notice : A2021-794 Affiliation des auteurs : LIF+Ext (2020- ) Thématique : BIODIVERSITE/FORET Nature : Article nature-HAL : ArtAvecCL-RevueNat DOI : 10.20870/revforfr.2021.4992 En ligne : https://doi.org/10.20870/revforfr.2021.4992 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99092
in Revue forestière française > vol 73 n° 1 (2021) . - pp 7 - 19[article]Documents numériques
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Quels besoins de connaissances pour le futur des forêts en France - pdf éditeurAdobe Acrobat PDF Spatially–encouraged spectral clustering: a technique for blending map typologies and regionalization / Levi John Wolf in International journal of geographical information science IJGIS, vol 35 n° 11 (November 2021)
[article]
Titre : Spatially–encouraged spectral clustering: a technique for blending map typologies and regionalization Type de document : Article/Communication Auteurs : Levi John Wolf, Auteur Année de publication : 2021 Article en page(s) : pp 2356 - 2373 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse de groupement
[Termes IGN] apprentissage automatique
[Termes IGN] exploration de données
[Termes IGN] méthode fondée sur le noyau
[Termes IGN] optimisation spatiale
[Termes IGN] régionalisation (segmentation)Résumé : (auteur) Clustering is a central concern in geographic data science and reflects a large, active domain of research. In spatial clustering, it is often challenging to balance two kinds of ‘goodness of fit:’ clusters should have ‘feature’ homogeneity, in that they aim to represent one ‘type’ of observation, and also ‘geographic’ coherence, in that they aim to represent some detected geographical ‘place’. This divides ‘map typologization’ studies, common in geodemographics, from ‘regionalization’ studies, common in spatial optimization and statistics. Recent attempts to simultaneously typologize and regionalize data into clusters with both feature homogeneity and geographic coherence have faced conceptual and computational challenges. Fortunately, new work on spectral clustering can address both regionalization and typologization tasks within the same framework. This research develops a novel kernel combination method for use within spectral clustering that allows analysts to blend smoothly between feature homogeneity and geographic coherence. I explore the formal properties of two kernel combination methods and recommend multiplicative kernel combination with spectral clustering. Altogether, spatially encouraged spectral clustering is shown as a novel kernel combination clustering method that can address both regionalization and typologization tasks in order to reveal the geographies latent in spatially structured data. Numéro de notice : A2021-762 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1934475 Date de publication en ligne : 05/07/2021 En ligne : https://doi.org/10.1080/13658816.2021.1934475 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98795
in International journal of geographical information science IJGIS > vol 35 n° 11 (November 2021) . - pp 2356 - 2373[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2021111 SL Revue Centre de documentation Revues en salle Disponible Utilisation de l’apprentissage profond dans la modélisation 3D urbaine : partie 2, post-traitement et évaluation / Hamza Ben Addou in Géomatique expert, n° 136 (novembre - décembre 2021)
[article]
Titre : Utilisation de l’apprentissage profond dans la modélisation 3D urbaine : partie 2, post-traitement et évaluation Type de document : Article/Communication Auteurs : Hamza Ben Addou, Auteur Année de publication : 2021 Article en page(s) : pp 42 -47 Note générale : Bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme d'apprentissage
[Termes IGN] apprentissage profond
[Termes IGN] CityGML
[Termes IGN] classification automatique d'objets
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] emprise au sol
[Termes IGN] maquette numérique
[Termes IGN] modèle 3D de l'espace urbain
[Termes IGN] modèle numérique du bâti
[Termes IGN] modélisation 3D
[Termes IGN] niveau de détail
[Termes IGN] orthoimage
[Termes IGN] primitive géométrique
[Termes IGN] toitRésumé : (Auteur) Post-traitement des données issues de l’algorithme d’apprentissage profond et modélisation 3D urbaine automatique Numéro de notice : A2021-919 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtSansCL DOI : sans Date de publication en ligne : 01/11/2021 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99335
in Géomatique expert > n° 136 (novembre - décembre 2021) . - pp 42 -47[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité IFN-001-P002286 PER Revue Nogent-sur-Vernisson Salle périodiques Exclu du prêt Détection des forêts dégradées en Guinée à partir des images satellites Sentinel-2 : évaluation de l'apport potentiel des nouveaux capteurs satellitaires optiques et radars / An Vo Quang in Blog de la RFPT, sans n° ([11/10/2021])
[article]
Titre : Détection des forêts dégradées en Guinée à partir des images satellites Sentinel-2 : évaluation de l'apport potentiel des nouveaux capteurs satellitaires optiques et radars Type de document : Article/Communication Auteurs : An Vo Quang, Auteur Année de publication : 2021 Note générale : Bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Afrique occidentale
[Termes IGN] apprentissage profond
[Termes IGN] classification dirigée
[Termes IGN] dégradation de l'environnement
[Termes IGN] dégradation de la flore
[Termes IGN] détection automatique
[Termes IGN] forêt alpestre
[Termes IGN] Guinée
[Termes IGN] image Sentinel-MSI
[Termes IGN] photo-interprétation
[Termes IGN] série temporelleRésumé : (Auteur) [Contexte] Les travaux de la thèse CIFRE ont été réalisés dans le cadre d'un partenariat entre l'Institut interdisciplinaire de recherche en énergie de Paris (LIED) et IGN FI, une société d'ingénierie géographique (partenaire export de l'IGN - Institut national de l’information géographique et forestière) qui réalise des projets sur tous les continents et dans tous les domaines d'application de la géomatique, notamment l'aménagement du territoire, l'environnement, l'agriculture, l'administration foncière ou la gestion des risques. Plus spécifiquement, les travaux de thèse se sont intégrés au projet de Zonage Agro-Ecologique de Guinée (ZAEG) coordonné par IGN FI et financé par l'Agence Française de Développement (AFD) pour le ministère de l’Agriculture de Guinée. Contrairement à la déforestation, la dégradation forestière implique un changement de la structure forestière sans modification de l'utilisation du sol. Ce changement est subtil et moins visible que la déforestation. La dégradation des forêts est une préoccupation majeure car un potentiel de séquestration du carbone est perdu. Ce phénomène varie en fonction de l'emplacement géographique, des facteurs anthropiques, du climat, des types de forêts impactées, donc il n'existe pas de méthodologie de détection unique pour cartographier la dégradation des forêts à l'échelle mondiale. En Guinée, le principal processus de dégradation est l'exploitation forestière sélective dans la forêt de massif, en plus de la fragmentation de la forêt causée par le changement d'utilisation des terres. L’objectif est d’optimiser les méthodes de photo-interprétation utilisées par IGN FI pour détecter les zones de forêt dégradée. Le suivi du couvert forestier à l'aide des méthodes traditionnelles de télédétection nécessite un coût important en termes d'expertise en photo-interprétation. Nous proposons une approche de suivi par une procédure de classification semi-automatisée avec un coût de photo-interprétation minimum en incluant le contexte pixellaire, en intégrant les données du capteur Sentinel-2, acquises de manière répétitive. Numéro de notice : A2021-679 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : sans Date de publication en ligne : 11/10/2021 En ligne : https://rfpt-sfpt.github.io/blog/sentinel-2/s%C3%A9rie%20temporelle/deep%20learn [...] Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99040
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10 (October 2021)PermalinkSpatial structure system of land use along urban rail transit based on GIS spatial clustering / Yu Gao in European journal of remote sensing, vol 54 sup 2 (2021)PermalinkSpatial thinking in cartography teaching for schoolchildren / Sonia Maria Vanzella Castellar in International journal of cartography, vol 7 n° 3 (October 2021)PermalinkThe integration of GPS/BDS real-time kinematic positioning and visual–inertial odometry based on smartphones / Zun Niu in ISPRS International journal of geo-information, vol 10 n° 10 (October 2021)PermalinkUnsupervised self-adaptive deep learning classification network based on the optic nerve microsaccade mechanism for unmanned aerial vehicle remote sensing image classification / Ming Cong in Geocarto international, vol 36 n° 18 ([01/10/2021])PermalinkAssessment and prediction of urban growth for a mega-city using CA-Markov model / Veerendra Yadav in Geocarto international, vol 36 n° 17 ([15/09/2021])PermalinkMapping canopy 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classification / Laxmi Narayana Eeti in Geocarto international, vol 36 n° 16 ([01/09/2021])PermalinkUtilisation de l'apprentissage profond dans la modélisation 3D urbaine [Partie 1] / Hamza Ben Addou in Géomatique expert, n° 135 (septembre 2021)PermalinkDeep learning-based image de-raining using discrete Fourier transformation / Prasen Kumar Sharma in The Visual Computer, vol 37 n° 8 (August 2021)PermalinkInvestigating the application of artificial intelligence for earthquake prediction in Terengganu / Suzlyana Marhain in Natural Hazards, vol 108 n° 1 (August 2021)PermalinkMapping essential urban land use categories with open big data: Results for five metropolitan areas in the United States of America / Bin Chen in ISPRS Journal of photogrammetry and remote sensing, vol 178 (August 2021)PermalinkMeasuring shallow-water bathymetric signal strength in lidar point attribute data using machine learning / Kim Lowell in International journal of geographical information science IJGIS, vol 35 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large-scale land cover mapping via weakly supervised deep learning / Qiutong Yu in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 6 (June 2021)PermalinkRetrieval of ultraviolet diffuse attenuation coefficients from ocean color using the kernel principal components analysis over ocean / Kunpeng Sun in IEEE Transactions on geoscience and remote sensing, vol 59 n° 6 (June 2021)PermalinkSimulating multi-exit evacuation using deep reinforcement learning / Dong Xu in Transactions in GIS, Vol 25 n° 3 (June 2021)PermalinkUncertainty management for robust probabilistic change detection from multi-temporal Geoeye-1 imagery / Mahmoud Salah in Applied geomatics, vol 13 n° 2 (June 2021)PermalinkA deep learning model using satellite ocean color and hydrodynamic model to estimate chlorophyll-a concentration / Daeyong Jin in Remote sensing, vol 13 n°10 (May-2 2021)PermalinkAutomatic detection and classification of low-level orographic precipitation processes from space-borne radars using machine learning / Malarvizhi Arulraj in Remote sensing of environment, vol 257 (May 2021)PermalinkCellular automata based land-use change simulation considering spatio-temporal influence heterogeneity of light rail transit construction: A case in Nanjing, China / Jiaming Na in ISPRS International journal of geo-information, vol 10 n° 5 (May 2021)PermalinkEstimation of some stand parameters from textural features from WorldView-2 satellite image using the artificial neural network and multiple regression methods: a case study from Turkey / Alkan Günlü in Geocarto international, vol 36 n° 8 ([01/05/2021])PermalinkIncreasing efficiency of the robust deformation analysis methods using genetic algorithm and generalised particle swarm optimisation / Mehmed Batilović in Survey review, Vol 53 n° 378 (May 2021)PermalinkIntegrating a forward feature selection algorithm, random forest, and cellular automata to extrapolate urban growth in the Tehran-Karaj region of Iran / Hossein 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algorithm / Vorapong Suppakitpaisarn in International journal of geographical information science IJGIS, vol 35 n° 5 (May 2021)PermalinkScalable deep learning to identify brick kilns and aid regulatory capacity / Jihyeon Lee in Proceedings of the National Academy of Sciences of the United States of America PNAS, vol 118 n° 17 (27 April 2021)PermalinkThe delineation of tea gardens from high resolution digital orthoimages using mean-shift and supervised machine learning methods / Akhtar Jamil in Geocarto international, vol 36 n° 7 ([15/04/2021])PermalinkUnsupervised multi-level feature extraction for improvement of hyperspectral classification / Qiaoqiao Sun in Remote sensing, vol 13 n° 8 (April-2 2021)PermalinkAnti-cross validation technique for constructing and boosting random subspace neural network ensembles for hyperspectral image classification / Laxmi Narayana Eeti in Geocarto international, vol 36 n° 6 ([01/04/2021])PermalinkAutomatic atmospheric correction for shortwave hyperspectral remote sensing data using a time-dependent deep neural network / Jian Sun in ISPRS Journal of photogrammetry and remote sensing, vol 174 (April 2021)PermalinkCloud detection from paired CrIS water vapor and CO₂ channels using machine learning techniques / Miao Tian in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)PermalinkA CNN approach to simultaneously count plants and detect plantation-rows from UAV imagery / Lucas Prado Osco in ISPRS Journal of photogrammetry and remote sensing, vol 174 (April 2021)PermalinkA convolutional neural network approach to predict non‐permissive environments from moderate‐resolution imagery / Seth Goodman in Transactions in GIS, Vol 25 n° 2 (April 2021)PermalinkDetecting ground deformation in the built environment using sparse satellite InSAR data with a convolutional neural network / Nantheera Anantrasirichai in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)PermalinkExtraction of sea ice cover by Sentinel-1 SAR based on support vector machine with unsupervised generation of training data / Xiao-Ming Li in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)PermalinkA geographic information-driven method and a new large scale dataset for remote sensing cloud/snow detection / Xi Wu in ISPRS Journal of photogrammetry and remote sensing, vol 174 (April 2021)PermalinkGraph convolutional networks by architecture search for PolSAR image classification / Hongying Liu in Remote sensing, vol 13 n° 7 (April-1 2021)PermalinkMachine learning and geodesy: A survey / Jemil Butt in Journal of applied geodesy, vol 15 n° 2 (April 2021)PermalinkParsing of urban facades from 3D point clouds based on a novel multi-view domain / Wei Wang in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 4 (April 2021)PermalinkPrecipitable water vapor fusion based on a generalized regression neural network / Bao Zhang in Journal of geodesy, vol 95 n° 4 (April 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automata and geo-informatics: comparison between Almaty and Astana in Kazakhstan / Aigerim Ilyassova in Geocarto international, vol 36 n° 5 ([15/03/2021])PermalinkAnalysis of plot-level volume increment models developed from machine learning methods applied to an uneven-aged mixed forest / Seyedeh Kosar Hamidi in Annals of Forest Science, vol 78 n° 1 (March 2021)PermalinkApplication of a multi-layer artificial neural network in a 3-D global electron density model using the long-term observations of COSMIC, Fengyun-3C, and Digisonde / Li Wang in Space weather, vol 19 n° 3 (March 2021)PermalinkAutomating and utilising equal-distribution data classification / Gennady Andrienko in International journal of cartography, vol 7 n° 1 (March 2021)PermalinkDetection of subpixel targets on hyperspectral remote sensing imagery based on background endmember extraction / Xiaorui Song in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 3 (March 2021)PermalinkDynamic human body reconstruction and motion tracking with low-cost depth cameras / Kangkan Wang in The Visual Computer, vol 37 n° 3 (March 2021)PermalinkFeature detection and description for image matching: from hand-crafted design to deep learning / Lin Chen in Geo-spatial Information Science, vol 24 n° 1 (March 2021)PermalinkA graph-based semi-supervised approach to classification learning in digital geographies / Pengyuan Liu in Computers, Environment and Urban Systems, vol 86 (March 2021)PermalinkGraph convolutional autoencoder model for the shape coding and cognition of buildings in maps / Xiongfeng Yan in International journal of geographical information science IJGIS, vol 35 n° 3 (March 2021)PermalinkLearning from GPS trajectories of floating car for CNN-based urban road extraction with high-resolution satellite imagery / Ju Zhang in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 3 (March 2021)PermalinkLightweight convolutional neural network-based pedestrian detection and re-identification in multiple scenarios / Xiao Ke in Machine Vision and Applications, vol 32 n° 2 (March 2021)PermalinkMachine learning in ground motion prediction / Farid Khosravikia in Computers & geosciences, vol 148 (March 2021)PermalinkMulti-level progressive parallel attention guided salient object detection for RGB-D images / Zhengyi Liu in The Visual Computer, vol 37 n° 3 (March 2021)PermalinkPBNet: Part-based convolutional neural network for complex composite object detection in remote sensing imagery / Xian Sun in ISPRS Journal of photogrammetry and remote sensing, vol 173 (March 2021)PermalinkRecognition of varying size scene images using semantic analysis of deep activation maps / Shikha Gupta in Machine Vision and Applications, vol 32 n° 2 (March 2021)PermalinkRobust unsupervised small area change detection from SAR imagery using deep learning / Xinzheng Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 173 (March 2021)PermalinkSuitability assessment of 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monitoring using SAR and optical satellite image time series / Puzhao Zhang (2021)PermalinkDescription et recherche d’image généralisables pour l’interconnexion et l’analyse multi-source / Dimitri Gominski (2021)PermalinkDétection d’ouvertures par segmentation sémantique de nuages de points 3D : apport de l’apprentissage profond / Camille Lhenry (2021)PermalinkDétection/reconnaissance d'objets urbains à partir de données 3D multicapteurs prises au niveau du sol, en continu / Younes Zegaoui (2021)PermalinkDétection et reconstruction 3D d’arbres urbains par segmentation de nuages de points : apport de l’apprentissage profond / Victor Alteirac (2021)PermalinkDynamic committee machine with fuzzy-c-means clustering for total organic carbon content prediction from wireline logs / Yang Bai in Computers & geosciences, vol 146 (January 2021)PermalinkEvaluation of a neural network with uncertainty for detection of ice and water in SAR imagery / Nazanin Asadi in IEEE Transactions on geoscience 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