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Experimental study on the atmospheric delay based on GPS, SAR Interferometry, and numerical weather model data / P. Mateus in IEEE Transactions on geoscience and remote sensing, vol 51 n° 1 Tome 1 (January 2013)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 51 n° 1 Tome 1 (January 2013) . - pp 6 - 11 Titre : Experimental study on the atmospheric delay based on GPS, SAR Interferometry, and numerical weather model data Type de document : Article/Communication Auteurs : P. Mateus, Auteur ; R. Tomé, Auteur ; J. Catalão, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 6 - 11 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] image Envisat-ASAR [Termes IGN] image radar moirée [Termes IGN] interféromètrie par radar à antenne synthétique [Termes IGN] Lisbonne [Termes IGN] modèle atmosphérique [Termes IGN] propagation du signal [Termes IGN] variation temporelle Résumé : (Auteur) In this paper, we present the results of an experiment aiming to compare measurements of atmospheric delay by synthetic aperture radar (SAR) interferometry and GPS techniques to estimates by numerical weather prediction. Maps of the differential atmospheric delay are generated by processing a set of interferometric SAR images acquired by the ENVISAT-ASAR mission over the Lisbon region from April to November 2009. GPS measurements of the wet zenith delay are carried out over the same area, covering the time interval between the first and the last SAR acquisition. The Weather Research and Forecasting (WRF) model is used to model the atmospheric delay over the study area at about the same time of SAR acquisitions. The analysis of results gives hints to devise mitigation approaches of atmospheric artifacts in SAR interferometry applications. Numéro de notice : A2013-009 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2200901 Date de publication en ligne : 23/07/2012 En ligne : https://doi.org/10.1109/TGRS.2012.2200901 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32147 [article]

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Tree species discrimination in tropical forests using airborne imaging spectroscopy / Jean-Baptiste Féret in IEEE Transactions on geoscience and remote sensing, vol 51 n° 1 Tome 1 (January 2013)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 51 n° 1 Tome 1 (January 2013) . - pp 73 - 84 Titre : Tree species discrimination in tropical forests using airborne imaging spectroscopy Type de document : Article/Communication Auteurs : Jean-Baptiste Féret, Auteur ; Gregory P. Asner, Auteur Année de publication : 2013 Article en page(s) : pp 73 - 84 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse discriminante [Termes IGN] arbre (flore) [Termes IGN] classification dirigée [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] distance de Bhattacharyya [Termes IGN] espèce végétale [Termes IGN] forêt tropicale [Termes IGN] Hawaii (Etats-Unis) [Termes IGN] image aérienne [Termes IGN] image hyperspectrale [Termes IGN] segmentation [Termes IGN] spectroscopie Résumé : (Auteur) We identify canopy species in a Hawaiian tropical forest using supervised classification applied to airborne hyperspectral imagery acquired with the Carnegie Airborne Observatory-Alpha system. Nonparametric methods (linear and radial basis function support vector machine, artificial neural network, and k-nearest neighbor) and parametric methods (linear, quadratic, and regularized discriminant analysis) are compared for a range of species richness values and training sample sizes. We find a clear advantage in using regularized discriminant analysis, linear discriminant analysis, and support vector machines. No unique optimal classifier was found for all conditions tested, but we highlight the possibility of improving support vector machine classification with a better optimization of its free parameters. We also confirm that a combination of spectral and spatial information increases accuracy of species classification: we combine segmentation and species classification from regularized discriminant analysis to produce a map of the 17 discriminated species. Finally, we compare different methods to assess spectral separability and find a better ability of Bhattacharyya distance to assess separability within and among species. The results indicate that species mapping is tractable in tropical forests when using high-fidelity imaging spectroscopy. Numéro de notice : A2013-010 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2199323 Date de publication en ligne : 16/07/2012 En ligne : https://doi.org/10.1109/TGRS.2012.2199323 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32148 [article]

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Improvement of stepped-frequency continuous wave Ground-Penetrating Radar cross-range resolution / I. Nicolaescu in IEEE Transactions on geoscience and remote sensing, vol 51 n° 1 Tome 1 (January 2013)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 51 n° 1 Tome 1 (January 2013) . - pp 85 - 92 Titre : Improvement of stepped-frequency continuous wave Ground-Penetrating Radar cross-range resolution Type de document : Article/Communication Auteurs : I. Nicolaescu, Auteur Année de publication : 2013 Article en page(s) : pp 85 - 92 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] antenne radar [Termes IGN] fouillis d'échos [Termes IGN] onde entretenue à fréquence échelonnée [Termes IGN] polarisation [Termes IGN] polarisation croisée [Termes IGN] radar pénétrant GPR Résumé : (Auteur) This paper presents some experimental results obtained with newly developed stepped-frequency continuous wave (SFCW) ground-penetrating radar in a frequency range from 400 to 4845 MHz. This paper describes the procedures to remove the delays within the transmitter, the receiver, and the antenna system and analyzes the experimental results obtained after processing the measured data. The measured system footprint and the experimental results show that using an ultrawideband (UWB) Archimedean spiral antenna decreases the coupling signal but does not provide the needed cross-range resolution. Based on the measured dimensions of the footprint, a synthetic aperture procedure is used to improve the radar cross-range resolution from around 60 cm to about 6 cm. This paper shows the ability of the radar to detect dielectric objects and to exhibit their shape as it works with circularly polarized waves. What makes this paper different from prior work, which presents some experiments using a vector network analyzer set up to work as an SFCW radar, is that the data were acquired with a real system working with circular polarization and using two UWB Archimedean spirals. The calibration procedures as well as the synthetic aperture procedure were developed and validated based on measured data with the SFCW radar. Numéro de notice : A2013-011 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2198069 Date de publication en ligne : 15/06/2012 En ligne : https://doi.org/ 10.1109/TGRS.2012.2198069 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32149 [article]

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Crop yield estimation based on unsupervised linear unmixing of multidate hyperspectral imagery / B. Luo in IEEE Transactions on geoscience and remote sensing, vol 51 n° 1 Tome 1 (January 2013)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 51 n° 1 Tome 1 (January 2013) . - pp 162 - 173 Titre : Crop yield estimation based on unsupervised linear unmixing of multidate hyperspectral imagery Type de document : Article/Communication Auteurs : B. Luo, Auteur ; C. Yang, Auteur ; Jocelyn Chanussot, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 162 - 173 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse des mélanges spectraux [Termes IGN] classification non dirigée [Termes IGN] image hyperspectrale [Termes IGN] image multitemporelle [Termes IGN] rendement agricole [Termes IGN] sorgho (céréale) Résumé : (Auteur) Hyperspectral imagery, which contains hundreds of spectral bands, has the potential to better describe the biological and chemical attributes on the plants than multispectral imagery and has been evaluated in this paper for the purpose of crop yield estimation. The spectrum of each pixel in a hyperspectral image is considered as a linear combinations of the spectra of the vegetation and the bare soil. Recently developed linear unmixing approaches are evaluated in this paper, which automatically extracts the spectra of the vegetation and bare soil from the images. The vegetation abundances are then computed based on the extracted spectra. In order to reduce the influences of this uncertainty and obtain a robust estimation results, the vegetation abundances extracted on two different dates on the same fields are then combined. The experiments are carried on the multidate hyperspectral images taken from two grain sorghum fields. The results show that the correlation coefficients between the vegetation abundances obtained by unsupervised linear unmixing approaches are as good as the results obtained by supervised methods, where the spectra of the vegetation and bare soil are measured in the laboratory. In addition, the combination of vegetation abundances extracted on different dates can improve the correlations (from 0.6 to 0.7). Numéro de notice : A2013-012 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2198826 Date de publication en ligne : 19/06/2012 En ligne : https://doi.org/10.1109/TGRS.2012.2198826 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32150 [article]

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Semisupervised local discriminant analysis for feature extraction in hyperspectral images / W. Liao in IEEE Transactions on geoscience and remote sensing, vol 51 n° 1 Tome 1 (January 2013)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 51 n° 1 Tome 1 (January 2013) . - pp 184 - 198 Titre : Semisupervised local discriminant analysis for feature extraction in hyperspectral images Type de document : Article/Communication Auteurs : W. Liao, Auteur ; A. Pizurica, Auteur ; Paul Scheunders, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 184 - 198 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse discriminante [Termes IGN] classification semi-dirigée [Termes IGN] extraction de traits caractéristiques [Termes IGN] image hyperspectrale [Termes IGN] matrice Résumé : (Auteur) We propose a novel semisupervised local discriminant analysis method for feature extraction in hyperspectral remote sensing imagery, with improved performance in both ill-posed and poor-posed conditions. The proposed method combines unsupervised methods (local linear feature extraction methods and supervised method (linear discriminant analysis) in a novel framework without any free parameters. The underlying idea is to design an optimal projection matrix, which preserves the local neighborhood information inferred from unlabeled samples, while simultaneously maximizing the class discrimination of the data inferred from the labeled samples. Experimental results on four real hyperspectral images demonstrate that the proposed method compares favorably with conventional feature extraction methods. Numéro de notice : A2013-013 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2200106 Date de publication en ligne : 28/06/2012 En ligne : https://doi.org/10.1109/TGRS.2012.2200106 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32151 [article]

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Semisupervised learning of hyperspectral data with unknown land-cover classes / G. Jun in IEEE Transactions on geoscience and remote sensing, vol 51 n° 1 Tome 1 (January 2013)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 51 n° 1 Tome 1 (January 2013) . - pp 273 - 282 Titre : Semisupervised learning of hyperspectral data with unknown land-cover classes Type de document : Article/Communication Auteurs : G. Jun, Auteur ; J. Ghosh, Auteur Année de publication : 2013 Article en page(s) : pp 273 - 282 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse de groupement [Termes IGN] apprentissage semi-dirigé [Termes IGN] Botswana [Termes IGN] classification bayesienne [Termes IGN] image hyperspectrale [Termes IGN] occupation du sol [Termes IGN] régression [Termes IGN] réponse spectrale [Termes IGN] variation Résumé : (Auteur) Both supervised and semisupervised algorithms for hyperspectral data analysis typically assume that all unlabeled data belong to the same set of land-cover classes that is represented by labeled data. This is not true in general, however, since there may be new classes in the unexplored regions within an image or in areas that are geographically near but topographically distinct. This problem is more likely to occur when one attempts to build classifiers that cover wider areas; such classifiers also need to address spatial variations in acquired spectral signatures if they are to be accurate and robust. This paper presents a semisupervised spatially adaptive mixture model (SESSAMM) to identify land covers from hyperspectral images in the presence of previously unknown land-cover classes and spatial variation of spectral responses. SESSAMM uses a nonparametric Bayesian framework to apply spatially adaptive mechanisms to the mixture model with (potentially) infinitely many components. In this method, each component in the mixture has spatially adapted parameters estimated by Gaussian process regression, and spatial correlations between indicator variables are also considered. The proposed SESSAMM algorithm is applied to hyperspectral data from Botswana and from the DC Mall, where some classes are present only in the unlabeled data. SESSAMM successfully differentiates unlabeled instances of previously known classes from unknown classes and provides better results than the standard Dirichlet process mixture model and other alternatives. Numéro de notice : A2013-014 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2198654 En ligne : https://doi.org/10.1109/TGRS.2012.2198654 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32152 [article]

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Updating land-cover maps by classification of image time series : A novel change-detection-driven transfer learning approach / Non-répertorié in IEEE Transactions on geoscience and remote sensing, vol 51 n° 1 Tome 1 (January 2013)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 51 n° 1 Tome 1 (January 2013) . - pp 300 - 312 Titre : Updating land-cover maps by classification of image time series : A novel change-detection-driven transfer learning approach Type de document : Article/Communication Auteurs : Non-répertorié, Auteur ; B. Demir, Auteur ; Francesca Bovolo, Auteur ; Lorenzo Bruzzone, Auteur Année de publication : 2013 Article en page(s) : pp 300 - 312 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse diachronique [Termes IGN] apprentissage dirigé [Termes IGN] carte d'occupation du sol [Termes IGN] classification automatique [Termes IGN] détection de changement [Termes IGN] image multitemporelle [Termes IGN] mise à jour de base de données [Termes IGN] série temporelle Résumé : (Auteur) This paper proposes a novel change-detection-driven transfer learning (TL) approach to update land-cover maps by classifying remote-sensing images acquired on the same area at different times (i.e., image time series). The proposed approach requires that a reliable training set is available only for one of the images (i.e., the source domain) in the time series whereas it is not for another image to be classified (i.e., the target domain). Unlike other literature TL methods, no additional assumptions on either the similarity between class distributions or the presence of the same set of land-cover classes in the two domains are required. The proposed method aims at defining a reliable training set for the target domain, taking advantage of the already available knowledge on the source domain. This is done by applying an unsupervised-change-detection method to target and source domains and transferring class labels of detected unchanged training samples from the source to the target domain to initialize the target-domain training set. The training set is then optimized by a properly defined novel active learning (AL) procedure. At the early iterations of AL, priority in labeling is given to samples detected as being changed, whereas in the remaining ones, the most informative samples are selected from changed and unchanged unlabeled samples. Finally, the target image is classified. Experimental results show that transferring the class labels from the source domain to the target domain provides a reliable initial training set and that the priority rule for AL results in a fast convergence to the desired accuracy with respect to Standard AL. Numéro de notice : A2013-015 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2195727 En ligne : https://doi.org/10.1109/TGRS.2012.2195727 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32153 [article]

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