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A merging solution for close-range DEMs to optimize surface coverage and measurement resolution / Stéphane Bertin in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 1 (January 2016)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 82 n° 1 (January 2016) . - pp 31 – 40 Titre : A merging solution for close-range DEMs to optimize surface coverage and measurement resolution Type de document : Article/Communication Auteurs : Stéphane Bertin, Auteur ; Heide Friedrich, Auteur ; Patrice Delmas, Auteur Année de publication : 2016 Article en page(s) : pp 31 – 40 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] étalonnage des données [Termes IGN] fusion de données [Termes IGN] gravier [Termes IGN] intégration de données [Termes IGN] lit [Termes IGN] modèle numérique de surface [Termes IGN] photogrammétrie métrologique [Termes IGN] torrent Résumé : (auteur) The process of efficient and effective DEM merging is increasingly becoming more important. To allow DEM analysis for features of different scales, an increase in surface coverage cannot result in reduced measurement resolution. It is thus inevitable that merging individual high-resolution DEMs will become common practice for applications such as hydraulic roughness studies for fluvial surfaces. This paper presents an efficient and effective merging solution, whereby accurate co-registration of individual DEMs collected from consistent viewpoints and standard averaging for overlapping elevations ensure seamless merging. The presented method is suitable for DEMs collected using any measurement technology, as long as individual DEMs overlap and are arranged on regular grids. The merging solution is applied to the study of a laboratory gravel bed measured with vertical stereo photogrammetry at the grain scale (>106 points/ m2). We show that the approach can be integrated into the DEM collection workflow at the design stage, which optimizes the measurement performance. We present how resampling before merging can be beneficial to keep data handling requirements practical, whilst ensuring accurate surface representation. Finally, the effect of scale variation is studied, showing that seamless merging applies to DEMs with variable resolution. Numéro de notice : A2016-049 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.83.1.31 En ligne : https://doi.org/10.14358/PERS.83.1.31 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79653 [article]

Estimation of forest biomass using multivariate relevance vector regression / Alireza Sharifi in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 1 (January 2016)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 82 n° 1 (January 2016) . - pp 41 - 49 Titre : Estimation of forest biomass using multivariate relevance vector regression Type de document : Article/Communication Auteurs : Alireza Sharifi, Auteur ; Jalal Amini, Auteur ; Ryutaro Tateishi, Auteur Année de publication : 2016 Article en page(s) : pp 41 - 49 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] biomasse aérienne [Termes IGN] biomasse forestière [Termes IGN] estimation statistique [Termes IGN] forêt [Termes IGN] image ALOS-PALSAR [Termes IGN] Iran [Termes IGN] Perceptron multicouche [Termes IGN] régression multiple [Termes IGN] séparateur à vaste marge [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) The objective of this study is to develop a method based on multivariate relevance vector regression (MVRVR) as a kernelbased Bayesian model for the estimation of above-ground biomass (AGB) in the Hyrcanian forests of Iran. Field AGB data from the Hyrcanian forests and multi-temporal PALSAR backscatter values are used for training and testing the methods. The results of the MVRVR method are then compared with other methods: multivariate linear regression (MLR), multilayer perceptron neural network (MLPNN), and support vector regression (SVR). The MLR model showed lower values of R2 than the three other approaches. Although the SVR model was found to be more accurate than MLPNN, it had the lowest saturation point of 224.75 Mg/ha. The use of MVRVR model significantly improves the estimation of AGB (R2 = 0.90; RMSE = 32.05 Mg/ha), and the model showed a superior performance in estimating AGB with the highest saturation point (297.81 Mg/ha). Numéro de notice : A2016-053 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.14358/PERS.83.1.41 En ligne : https://doi.org/10.14358/PERS.83.1.41 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79654 [article]