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Comprehensive evaluation of soil moisture retrieval models under different crop cover types using C-band synthetic aperture radar data / P. Kumar in Geocarto international, vol 34 n° 9 ([15/06/2019])  

Public [article]  inGeocarto international > vol 34 n° 9 [15/06/2019] . - pp 1022-1041 Titre : Comprehensive evaluation of soil moisture retrieval models under different crop cover types using C-band synthetic aperture radar data Type de document : Article/Communication Auteurs : P. Kumar, Auteur ; A. Choudhary, Auteur ; D. K. Gupta, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 1022-1041 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] bande C [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] couvert végétal [Termes descripteurs IGN] échantillonnage d'image [Termes descripteurs IGN] humidité du sol [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] image Radarsat [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] modèle de régression [Termes descripteurs IGN] polarisation [Termes descripteurs IGN] réseau neuronal artificiel [Termes descripteurs IGN] séparateur à vaste marge [Termes descripteurs IGN] Uttar Pradesh (Inde ; état) Résumé : (auteur) In the present study, random forest regression (RFR), support vector regression (SVR) and artificial neural network regression (ANNR) models were evaluated for the retrieval of soil moisture covered by winter wheat, barley and corn crops. SVR with radial basis function kernel was provided the highest adj. R2 (0.95) value for soil moisture retrieval covered by the wheat crop at VV polarization. However, RFR provided the adj. R2 (0.94) value for soil moisture retrieval covered by barley crop at VV polarization using Sentinel-1A satellite data. The adj. R2 (0.94) values were found for the soil moisture covered by corn crop at VV polarization using RFR, SVR linear and radial basis function kernels. The least performance was reported using ANNR model for almost all the crops under investigation. The soil moisture retrieval outcomes were found better at VV polarization in comparison to VH polarization using three different models. Numéro de notice : A2019-517 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1464601 date de publication en ligne : 03/05/2018 En ligne : https://doi.org/10.1080/10106049.2018.1464601 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93876 [article]

Above-bottom biomass retrieval of aquatic plants with regression models and SfM data acquired by a UAV platform – A case study in Wild Duck Lake Wetland, Beijing, China / Ran Jing in ISPRS Journal of photogrammetry and remote sensing, vol 134 (December 2017)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 134 (December 2017) . - pp 122 - 134 Titre : Above-bottom biomass retrieval of aquatic plants with regression models and SfM data acquired by a UAV platform – A case study in Wild Duck Lake Wetland, Beijing, China Type de document : Article/Communication Auteurs : Ran Jing, Auteur ; Zhaoning Gong, Auteur ; Wenji Zhao, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 122 - 134 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] arbre de décision [Termes descripteurs IGN] biomasse [Termes descripteurs IGN] croissance végétale [Termes descripteurs IGN] drone [Termes descripteurs IGN] flore aquatique [Termes descripteurs IGN] image aérienne [Termes descripteurs IGN] indice de végétation [Termes descripteurs IGN] lac [Termes descripteurs IGN] modèle de régression [Termes descripteurs IGN] orthoimage [Termes descripteurs IGN] Pékin (Chine) [Termes descripteurs IGN] régression linéaire [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] structure-from-motion [Termes descripteurs IGN] zone humide Résumé : (Auteur) Above-bottom biomass (ABB) is considered as an important parameter for measuring the growth status of aquatic plants, and is of great significance for assessing health status of wetland ecosystems. In this study, Structure from Motion (SfM) technique was used to rebuild the study area with high overlapped images acquired by an unmanned aerial vehicle (UAV). We generated orthoimages and SfM dense point cloud data, from which vegetation indices (VIs) and SfM point cloud variables including average height (HAVG), standard deviation of height (HSD) and coefficient of variation of height (HCV) were extracted. These VIs and SfM point cloud variables could effectively characterize the growth status of aquatic plants, and thus they could be used to develop a simple linear regression model (SLR) and a stepwise linear regression model (SWL) with field measured ABB samples of aquatic plants. We also utilized a decision tree method to discriminate different types of aquatic plants. The experimental results indicated that (1) the SfM technique could effectively process high overlapped UAV images and thus be suitable for the reconstruction of fine texture feature of aquatic plant canopy structure; and (2) an SWL model based on point cloud variables: HAVG, HSD, HCV and two VIs: NGRDI, ExGR as independent variables has produced the best predictive result of ABB of aquatic plants in the study area, with a coefficient of determination of 0.84 and a relative root mean square error of 7.13%. In this analysis, a novel method for the quantitative inversion of a growth parameter (i.e., ABB) of aquatic plants in wetlands was demonstrated. Numéro de notice : A2017-732 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.11.002 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.11.002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88431 [article]

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Thorough statistical comparison of machine learning regression models and their ensembles for sub-pixel imperviousness and imperviousness change mapping / Wojciech Drzewiecki in Geodesy and cartography, vol 66 n° 2 (December 2017)  

Public [article]  inGeodesy and cartography > vol 66 n° 2 (December 2017) . - pp 171 - 210 Titre : Thorough statistical comparison of machine learning regression models and their ensembles for sub-pixel imperviousness and imperviousness change mapping Type de document : Article/Communication Auteurs : Wojciech Drzewiecki, Auteur Année de publication : 2017 Article en page(s) : pp 171 - 210 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse diachronique [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] bassin hydrographique [Termes descripteurs IGN] classification pixellaire [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] image Landsat [Termes descripteurs IGN] modèle de régression [Termes descripteurs IGN] Pologne [Termes descripteurs IGN] surface imperméable Résumé : (auteur) We evaluated the performance of nine machine learning regression algorithms and their ensembles for sub-pixel estimation of impervious areas coverages from Landsat imagery. The accuracy of imperviousness mapping in individual time points was assessed based on RMSE, MAE and R2. These measures were also used for the assessment of imperviousness change intensity estimations. The applicability for detection of relevant changes in impervious areas coverages at sub-pixel level was evaluated using overall accuracy, F-measure and ROC Area Under Curve. The results proved that Cubist algorithm may be advised for Landsat-based mapping of imperviousness for single dates. Stochastic gradient boosting of regression trees (GBM) may be also considered for this purpose. However, Random Forest algorithm is endorsed for both imperviousness change detection and mapping of its intensity. In all applications the heterogeneous model ensembles performed at least as well as the best individual models or better. They may be recommended for improving the quality of sub-pixel imperviousness and imperviousness change mapping. The study revealed also limitations of the investigated methodology for detection of subtle changes of imperviousness inside the pixel. None of the tested approaches was able to reliably classify changed and non-changed pixels if the relevant change threshold was set as one or three percent. Also for fi ve percent change threshold most of algorithms did not ensure that the accuracy of change map is higher than the accuracy of random classifi er. For the threshold of relevant change set as ten percent all approaches performed satisfactory. Numéro de notice : A2017-787 Thématique : IMAGERIE Nature : Article DOI : 10.1515/geocart-2017-0012 En ligne : https://doi.org/10.1515/geocart-2017-0012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89099 [article]

Adaptive and optimal combination of local features for image retrieval / Neelanjan Bhowmik (2017)    

Public contenu dans The 23rd international conference on MultiMedia Modeling, MMM 2017 / Laurent Amsaleg (2017)   Titre : Adaptive and optimal combination of local features for image retrieval Type de document : Article/Communication Auteurs : Neelanjan Bhowmik , Auteur ; Valérie Gouet-Brunet , Auteur ; Lijun Wei , Auteur ; Gabriel Bloch, Auteur Congrès : MMM 2017, 23rd international conference on Multimedia Modeling (4 - 6 janvier 2017; Campus of Reykjavik University, Reykjavik, Islande), Commanditaire Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2017 Autre Editeur : Saint-Mandé : Institut national de l'information géographique et forestière - IGN Collection : Lecture notes in Computer Science, ISSN 0302-9743 Projets : POEME / Gouet-Brunet, Valérie Importance : pp 76 - 88 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] modèle de régression [Termes descripteurs IGN] point d'intérêt [Termes descripteurs IGN] recherche d'image basée sur le contenu Résumé : (Auteur) With the large number of local feature detectors and descriptors in the literature of Content-Based Image Retrieval (CBIR), in this work we propose a solution to predict the optimal combination of features, for improving image retrieval performances, based on the spatial complementarity of interest point detectors. We review several complementarity criteria of detectors and employ them in a regression based prediction model, designed to select the suitable detectors combination for a dataset. The proposal can improve retrieval performance even more by selecting optimal combination for each image (and not only globally for the dataset), as well as being profitable in the optimal fitting of some parameters. The proposal is appraised on three state-of-the-art datasets to validate its effectiveness and stability. The experimental results highlight the importance of spatial complementarity of the features to improve retrieval, and prove the advantage of using this model to optimally adapt detectors combination and some parameters. Numéro de notice : C2017-021 Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1007/978-3-319-51814-5_7 date de publication en ligne : 01/06/2017 En ligne : https://doi.org/10.1007/978-3-319-51814-5_7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88988

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Cross-domain image localization by adaptive feature fusion / Neelanjan Bhowmik (2017)  

Public Titre : Cross-domain image localization by adaptive feature fusion Type de document : Article/Communication Auteurs : Neelanjan Bhowmik , Auteur ; Li Weng , Auteur ; Valérie Gouet-Brunet , Auteur ; Bahman Soheilian , Auteur Congrès : JURSE 2017, Joint urban remote sensing event (6 - 8 mars 2017; Dubai) , Commanditaire Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2017 Importance : 4 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] appariement d'images [Termes descripteurs IGN] environnement de développement [Termes descripteurs IGN] estimation de pose [Termes descripteurs IGN] géopositionnement [Termes descripteurs IGN] modèle de régression [Termes descripteurs IGN] recherche d'information géographique [Termes descripteurs IGN] similitude Mots-clés libres : Content-Based Image Retrieval Résumé : (auteur) We address the problem of cross-domain image localization, i.e., the ability of estimating the pose of a landmark from visual content acquired under various conditions, such as old photographs, paintings, photos taken at a particular season, etc. We explore a 2D approach where the pose is estimated from geo-localized reference images that visually match the query image. This work focuses on the retrieval of similar images, which is a challenging task for images across different domains. We propose a Content-Based Image Retrieval (CBIR) framework that adaptively combines multiple image descriptions. A regression model is used to select the best feature combinations according to their spatial complementarity, globally for a whole dataset as well as adaptively for each given image. The framework is evaluated on different datasets and the experiments prove its advantage over classical retrieval approaches. Numéro de notice : C2017-028 Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/JURSE.2017.7924572 date de publication en ligne : 11/05/2017 En ligne : https://doi.org/10.1109/JURSE.2017.7924572 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89292

A new climatology of maximum and minimum temperature (1951–2010) in the Spanish mainland: a comparison between three different interpolation methods / D. Peña-Angulo in International journal of geographical information science IJGIS, vol 30 n° 11-12 (November - December 2016)  

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A functional regression model for inventories supported by aerial laser scanner data or photogrammetric point clouds / Magnussen, Steen in Remote sensing of environment, vol 184 (October 2016)  

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Disaggregation of remotely sensed soil moisture in heterogeneous landscapes using holistic structure-based models / Subit Chakrabarti in IEEE Transactions on geoscience and remote sensing, vol 54 n° 8 (August 2016)  

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A bootstrap test for constant coefficients in geographically weighted regression models / Chang-Lin Mei in International journal of geographical information science IJGIS, vol 30 n° 7- 8 (July - August 2016)  

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Approximating prediction uncertainty for random forest regression models / John W. Coulston in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 3 (March 2016)

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Comparison of three Landsat TM compositing methods: A case study using modeled tree canopy cover / Bonnie Ruefenacht in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 3 (March 2016)

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A phase space reconstruction based single channel ICA algorithm and its application in dam deformation analysis / W. Dai in Survey review, vol 47 n° 345 (November 2015)  

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Estimating the count of completeness errors in geographic data sets by means of a generalized Waring regression model / Francisco Javier Ariza-López in International journal of geographical information science IJGIS, vol 29 n° 8 (August 2015)  

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Using characteristic spectral bands of OMIS1 imaging spectrometer to retrieve urban land surface temperature / S.Y. Zhu in International Journal of Remote Sensing IJRS, vol 27 n°7-8 (April 2006)

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A method for detecting large-scale forest covers change using coarse spatial resolution imagery / R.H. Fraser in Remote sensing of environment, vol 95 n° 4 (30/04/2005)

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