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Potential of Bayesian formalism for the fusion and assimilation of sequential forestry data in time and space / Cheikh Mohamedou in Canadian Journal of Forest Research, Vol 52 n° 4 (April 2022)  

Public [article]  inCanadian Journal of Forest Research > Vol 52 n° 4 (April 2022) . - pp 439 - 449 Titre : Potential of Bayesian formalism for the fusion and assimilation of sequential forestry data in time and space Type de document : Article/Communication Auteurs : Cheikh Mohamedou, Auteur ; Annika S. Kangas, Auteur ; Alireza Hamedianfar, Auteur ; Jari Vauhkonen, Auteur Année de publication : 2022 Article en page(s) : pp 439 - 449 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] données spatiotemporelles [Termes IGN] dynamique de la végétation [Termes IGN] estimation bayesienne [Termes IGN] fusion de données [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] série temporelle [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) Forest resource assessments based on multi-source and multi-temporal data have become more common. Therefore, enhancing the prediction capabilities of forestry dynamics by efficiently pooling and analyzing time-series and spatial sequential data is now more pivotal. Bayesian filtering and smoothing provide a well-defined formalism for the fusion or assimilation of various data. We ascertained how often the generic, standardized Bayesian framework is used in the scientific literature and whether such an approach is beneficial for forestry applications. A review of the literature showed that the use of Bayesian methods appears to be less common in forestry than in other disciplines, particularly remote sensing. Specifically, time-series analyses were found to favor ad hoc methods. Our review did not reveal strong numeric evidence for better performance by the various Bayesian approaches, but this result may be partly due to the challenge in comparing a variety of methods for different prediction tasks. We identified methodological challenges related to assimilating predictions of forest development; in particular, combining modelled growth with disturbances due to both forest operations and natural phenomena. Nevertheless, the Bayesian frameworks provide possibilities to efficiently combine and update prior and posterior predictive distributions and derive related uncertainty measures that appear under-utilized in forestry. Numéro de notice : A2022-315 Affiliation des auteurs : non IGN Thématique : FORET/MATHEMATIQUE Nature : Article DOI : 10.1139/cjfr-2021-0145 Date de publication en ligne : 17/01/2022 En ligne : https://doi.org/10.1139/cjfr-2021-0145 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100415 [article]

Synergistic use of particle swarm optimization, artificial neural network, and extreme gradient boosting algorithms for urban LULC mapping from WorldView-3 images / Alireza Hamedianfar in Geocarto international, vol 37 n° 3 ([01/02/2022])  

Public [article]  inGeocarto international > vol 37 n° 3 [01/02/2022] . - pp 773 - 791 Titre : Synergistic use of particle swarm optimization, artificial neural network, and extreme gradient boosting algorithms for urban LULC mapping from WorldView-3 images Type de document : Article/Communication Auteurs : Alireza Hamedianfar, Auteur ; Mohamed Barakat A. Gibril, Auteur ; Mohammadjavad Hosseinpoor, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 773 - 791 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse d'image orientée objet [Termes IGN] carte d'occupation du sol [Termes IGN] Extreme Gradient Machine [Termes IGN] image à très haute résolution [Termes IGN] image Worldview [Termes IGN] itération [Termes IGN] optimisation (mathématiques) [Termes IGN] optimisation par essaim de particules [Termes IGN] réseau neuronal artificiel [Termes IGN] segmentation d'image [Termes IGN] zone urbaine Résumé : (auteur) Geographic object-based image analysis (GEOBIA) has emerged as an effective and evolving paradigm for analyzing very high resolution (VHR) images as it demonstrates preeminence over the traditional pixel-wise methods and enables the utilization of diverse spectral, geometrical, and textural information to for image classification. Among feature selection (FS) methods, metaheuristic FS techniques have recently demonstrated effective performance in the dimensionality reduction of GEOBIA features. In this study, an artificial neural network (ANN) was integrated with particle swarm optimization (PSO) to enhance the learning process and more effectively determine the most significant features and their importance using WorldView-3 (WV-3) satellite data. First, multi-resolution image segmentation parameters were tuned using Taguchi optimization technique and unsupervised segmentation quality measure. Second, the proposed ANN–PSO was compared with PSO under 100 iterations. The ANN–PSO integration achieved lower root mean square error (RMSE) in all the iterations. Third, state-of-the-art extreme gradient boosting (Xgboost) image classifier was used to derive the land use/land cover (LULC) map of the first study area and assess the transferability of the selected features on the second and third regions. The Xgboost classifier obtained 91.68%, 89.54%, and 89.33% overall accuracies for the first, second, and third sites, respectively. ANN contributed to an intelligent approach for identifying which features are more likely to be relevant and discriminate the land cover types. The proposed integrated FS is a promising approach and an efficient tool for determining significant features and enhancing the detection of urban LULC classes from WV-3 data. Numéro de notice : A2022-344 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1737974 Date de publication en ligne : 12/03/2020 En ligne : https://doi.org/10.1080/10106049.2020.1737974 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100525 [article]

Development of fuzzy rule-based parameters for urban object-oriented classification using very high resolution imagery / Alireza Hamedianfar in Geocarto international, vol 29 n° 3 - 4 (June - July 2014)  

Public [article]  inGeocarto international > vol 29 n° 3 - 4 (June - July 2014) . - pp. 268 - 292 Titre : Development of fuzzy rule-based parameters for urban object-oriented classification using very high resolution imagery Type de document : Article/Communication Auteurs : Alireza Hamedianfar, Auteur ; Helmi Zulhaidi Mohd Shafri, Auteur Année de publication : 2014 Article en page(s) : pp. 268 - 292 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] classification floue [Termes IGN] classification orientée objet [Termes IGN] image Worldview [Termes IGN] Malaisie [Termes IGN] zone urbaine Résumé : (Auteur) Urban areas consist of spectrally and spatially heterogeneous features. Advanced information extraction techniques are needed to handle high resolution imageries in providing detailed information for urban planning applications. This study was conducted to identify a technique that accurately maps impervious and pervious surfaces from WorldView-2 (WV-2) imagery. Supervised per-pixel classification algorithms including Maximum Likelihood and Support Vector Machine (SVM) were utilized to evaluate the capability of spectral-based classifiers to classify urban features. Object-oriented classification was performed using supervised SVM and fuzzy rule-based approach to add spatial and texture attributes to spectral information. Supervised object-oriented SVM achieved 82.80% overall accuracy which was the better accuracy compared to supervised per-pixel classifiers. Classification based on the proposed fuzzy rule-based system revealed satisfactory output compared to other classification techniques with an overall accuracy of 87.10% for pervious surfaces and an overall accuracy of 85.19% for impervious surfaces. Numéro de notice : A2014-339 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2012.760006 En ligne : https://doi.org/10.1080/10106049.2012.760006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=73707 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2014021	RAB	Revue	Centre de documentation	En réserve L003	Disponible