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Soil roughness retrieval from TerraSar-X data using neural network and fractal method / Mohammad Maleki in Advances in space research, vol 64 n°5 (1 September 2019)  

Public [article]  inAdvances in space research > vol 64 n°5 (1 September 2019) . - pp 1117-1129 Titre : Soil roughness retrieval from TerraSar-X data using neural network and fractal method Type de document : Article/Communication Auteurs : Mohammad Maleki, Auteur ; Jalal Amini, Auteur ; Claudia Notarnicola, Auteur Année de publication : 2019 Article en page(s) : pp 1117-1129 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] analyse fractale [Termes IGN] bande X [Termes IGN] équation intégrale [Termes IGN] image TerraSAR-X [Termes IGN] modèle d'inversion [Termes IGN] modèle numérique de terrain [Termes IGN] Perceptron multicouche [Termes IGN] polarimétrie radar [Termes IGN] rugosité du sol Résumé : (auteur) The purpose of this study is to estimate the surface roughness (rms) using TerraSar-X data in HH polarization. Simulation of data is carried out at a wide range of moisture and roughness using the Integral Equation Model (IEM). The inversion method is based on Multi-Layer Perceptron neural network. Inversion technique is performed in two steps. In the first step, the neural network is trained using synthetic data. The inputs of the first neural network are the backscattering coefficient and incidence angle, and the moisture is the output. In the next step, three neural networks are built based on a prior and without prior information on roughness. The inputs of three neural network are backscattering coefficient, estimated moisture in the first step and incidence angle and the roughness is output. The validation of the proposed methods is carried out based on synthetic and real data. Ground roughness measurements are extracted from Digital Terrain Model (DTM) using the fractal method. The accuracy of moisture from synthetic data is 6.1 vol% without prior information on moisture and roughness. The roughness (rms) accuracy of synthetic datasets is 0. 61 cm without prior information and is 0.31 cm and 0.38 cm for rms lower than 2 cm and rms between 2 and 4 cm, with prior information on roughness. The result's analysis of the simulated data showed that the prior information on roughness strongly improves the accuracy of roughness and moisture estimates. The accuracy of rms estimates for the TerraSar-X image in the HH polarization is about 0.9 cm in the case of no prior information on roughness. The accuracy improves to 0.57 cm for rms lower than 2 cm and 0.54 cm for rms between 2 and 4 cm with prior information on roughness. An overestimation of rms for rms lower than 2 cm and an underestimation of rms for rms higher than 2 cm are observed. The results of the accuracy of the synthetic and real data showed that the X band in HH polarization has a very good potential to estimate the soil roughness. Numéro de notice : A2019-411 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.asr.2019.04.019 Date de publication en ligne : 24/04/2019 En ligne : https://doi.org/10.1016/j.asr.2019.04.019 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93527 [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]