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Soil moisture retrieval in agricultural fields using adaptive model-based polarimetric decomposition of SAR data / Lian He in IEEE Transactions on geoscience and remote sensing, vol 54 n° 8 (August 2016)
[article]
Titre : Soil moisture retrieval in agricultural fields using adaptive model-based polarimetric decomposition of SAR data Type de document : Article/Communication Auteurs : Lian He, Auteur ; Rocco Panciera, Auteur Année de publication : 2016 Article en page(s) : pp 4445 - 4460 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] bande L
[Termes IGN] biomasse
[Termes IGN] cultures
[Termes IGN] décomposition d'image
[Termes IGN] données polarimétriques
[Termes IGN] filtre adaptatif
[Termes IGN] humidité du sol
[Termes IGN] image radar moirée
[Termes IGN] polarimétrie radarRésumé : (Auteur) The aim of this paper was to estimate soil moisture in agricultural crop fields from fully polarimetric L-band synthetic aperture radar (SAR) data through the polarimetric decomposition of the SAR coherency matrix. A nonnegative-eigenvalue-decomposition scheme, together with an adaptive volume scattering model, is extended to an adaptive model-based decomposition (MBD) (Adaptive MBD) model for soil moisture retrieval. The Adaptive MBD can ensure nonnegative decomposed scattering components and allows two parameters (i.e., the mean orientation angle and a degree of randomness) to be determined to characterize the volume scattering. Its performance was tested using airborne SAR data and coincident ground measurements collected over agricultural fields in southeastern Australia and compared with previous MBD methods (i.e., the Freeman three-component decomposition using the extended Bragg model, the Yamaguchi three-component decomposition, and an iterative generalized hybrid decomposition). The results obtained with the newly proposed decomposition scheme agreed well with expectations based on observed plant structure and biomass levels. The new method was superior in tracking soil moisture dynamics with respect to previous decomposition methods in our study area, with root-mean-square error of soil moisture estimations being 0.10 and 0.14 m3/m3, respectively, for surface and double-bounce components. However, large variability in the achieved soil moisture accuracy was observed, depending on the presence of row structures in the underlying soil surface. Numéro de notice : A2016-884 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2542214 En ligne : https://doi.org/10.1109/TGRS.2016.2542214 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83048
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 8 (August 2016) . - pp 4445 - 4460[article]Multiple morphological component analysis based decomposition for remote sensing image classification / Xiang Xu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 5 (May 2016)
[article]
Titre : Multiple morphological component analysis based decomposition for remote sensing image classification Type de document : Article/Communication Auteurs : Xiang Xu, Auteur ; Jun Li, Auteur ; Xin Huang, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 3083 - 3102 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification
[Termes IGN] décomposition d'image
[Termes IGN] image multi sources
[Termes IGN] morphologie mathématiqueRésumé : (Auteur) Remote sensing images exhibit significant contrast and intensity regions and edges, which makes them highly suitable for using different texture features to properly represent and classify the objects that they contain. In this paper, we present a new technique based on multiple morphological component analysis (MMCA) that exploits multiple textural features for decomposition of remote sensing images. The proposed MMCA framework separates a given image into multiple pairs of morphological components (MCs) based on different textural features, with the ultimate goal of improving the signal-to-noise level and the data separability. A distinguishing feature of our proposed approach is the possibility to retrieve detailed image texture information, rather than using a single spatial characteristic of the texture. In this paper, four textural features: content, coarseness, contrast, and directionality (including horizontal and vertical), are considered for generating the MCs. In order to evaluate the obtained MCs, we conduct classification by using both remotely sensed hyperspectral and polarimetric synthetic aperture radar (SAR) scenes, showing the capacity of the proposed method to deal with different kinds of remotely sensed images. The obtained results indicate that the proposed MMCA framework can lead to very good classification performances in different analysis scenarios with limited training samples. Numéro de notice : A2016-848 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2511197 En ligne : https://doi.org/10.1109/TGRS.2015.2511197 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82929
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 5 (May 2016) . - pp 3083 - 3102[article]Compressive sensing for multibaseline polarimetric SAR tomography of forested areas / Xinwu Li in IEEE Transactions on geoscience and remote sensing, vol 54 n° 1 (January 2016)
[article]
Titre : Compressive sensing for multibaseline polarimetric SAR tomography of forested areas Type de document : Article/Communication Auteurs : Xinwu Li, Auteur ; Lei Liang, Auteur ; Huadong Guo, Auteur ; Yue Huang, Auteur Année de publication : 2016 Article en page(s) : pp 153 - 166 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] bande P
[Termes IGN] compression d'image
[Termes IGN] décomposition d'image
[Termes IGN] données polarimétriques
[Termes IGN] forêt tropicale
[Termes IGN] Guyane (département français)
[Termes IGN] image aérienne
[Termes IGN] image radar moirée
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] réflectivité
[Termes IGN] tomographie radarRésumé : (Auteur) The structure of forests is an important indicator of ecosystem dynamics and enables the modeling and monitoring of ecological change. Synthetic aperture radar tomography (TomoSAR) provides scene reflectivity estimation of vegetation along elevation coordinates. Due to the advantages of superresolution imaging and a small number of measurements, compressive sensing (CS) inversion techniques for SAR tomography were successfully developed and applied. This paper addresses the 3-D imaging of forested areas based on the framework of CS using fully polarimetric (FP) multibaseline SAR interferometric (MB-InSAR) tomography at P-band. A new CS-based FP MB-InSAR tomography method is proposed: a sum of Kronecker product (SKP) decomposition-based CS FP MB-InSAR tomography method (FP-SKPCS TomoSAR method). The method, based on an assumption that the reflectivity signal of a single scattering mechanism (SM) is more sparse than that of a composite of SMs, recovers the reflectivity profile of different SMs by using the CS technique. This method not only allows superresolution imaging with a low number of acquisitions but also can estimate the polarimetric SM of the vertical structure of forested areas. The effectiveness of these novel techniques for polarimetric SAR tomography is demonstrated using FP P-band airborne data sets acquired by the ONERA SETHI airborne system over a test site in Paracou, French Guiana, and the results of the vertical structure of forested areas derived by the method are verified by in situ test data. Numéro de notice : A2016-076 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2451992 En ligne : http://dx.doi.org/10.1109/TGRS.2015.2451992 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79844
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 1 (January 2016) . - pp 153 - 166[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2016011 SL Revue Centre de documentation Revues en salle Disponible Linear models for airborne-laser-scanning-based operational forest inventory with small field sample size and highly correlated LiDAR data / Virpi Junttila in IEEE Transactions on geoscience and remote sensing, vol 53 n° 10 (October 2015)
[article]
Titre : Linear models for airborne-laser-scanning-based operational forest inventory with small field sample size and highly correlated LiDAR data Type de document : Article/Communication Auteurs : Virpi Junttila, Auteur ; Tuomo Kauranne, Auteur ; Andrew O. Finley, Auteur ; John B. Bradford, Auteur Année de publication : 2015 Article en page(s) : pp 5600 - 5612 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] appariement d'images
[Termes IGN] décomposition d'image
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] modèle linéaire
[Termes IGN] placette d'échantillonnage
[Termes IGN] précision des données
[Vedettes matières IGN] Inventaire forestierRésumé : (Auteur) Modern operational forest inventory often uses remotely sensed data that cover the whole inventory area to produce spatially explicit estimates of forest properties through statistical models. The data obtained by airborne light detection and ranging (LiDAR) correlate well with many forest inventory variables, such as the tree height, the timber volume, and the biomass. To construct an accurate model over thousands of hectares, LiDAR data must be supplemented with several hundred field sample measurements of forest inventory variables. This can be costly and time consuming. Different LiDAR-data-based and spatial-data-based sampling designs can reduce the number of field sample plots needed. However, problems arising from the features of the LiDAR data, such as a large number of predictors compared with the sample size (overfitting) or a strong correlation among predictors (multicollinearity), may decrease the accuracy and precision of the estimates and predictions. To overcome these problems, a Bayesian linear model with the singular value decomposition of predictors, combined with regularization, is proposed. The model performance in predicting different forest inventory variables is verified in ten inventory areas from two continents, where the number of field sample plots is reduced using different sampling designs. The results show that, with an appropriate field plot selection strategy and the proposed linear model, the total relative error of the predicted forest inventory variables is only 5%-15% larger using 50 field sample plots than the error of a linear model estimated with several hundred field sample plots when we sum up the error due to both the model noise variance and the model's lack of fit. Numéro de notice : A2015-748 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2425916 Date de publication en ligne : 14/05/2015 En ligne : https://doi.org/10.1109/TGRS.2015.2425916 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78757
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 10 (October 2015) . - pp 5600 - 5612[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2015101 SL Revue Centre de documentation Revues en salle Disponible Hyperspectral and multispectral image fusion based on a sparse representation / Qi Wei in IEEE Transactions on geoscience and remote sensing, vol 53 n° 7 (July 2015)
[article]
Titre : Hyperspectral and multispectral image fusion based on a sparse representation Type de document : Article/Communication Auteurs : Qi Wei, Auteur ; José Bioucas-Dias, Auteur ; Nicolas Dobigeon, Auteur ; Jean-Yves Tourneret, Auteur Année de publication : 2015 Article en page(s) : pp 3658 - 3668 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] décomposition d'image
[Termes IGN] fusion d'images
[Termes IGN] image hyperspectrale
[Termes IGN] image multibande
[Termes IGN] optimisation (mathématiques)
[Termes IGN] problème inverse
[Termes IGN] représentation parcimonieuseRésumé : (Résumé) This paper presents a variational-based approach for fusing hyperspectral and multispectral images. The fusion problem is formulated as an inverse problem whose solution is the target image assumed to live in a lower dimensional subspace. A sparse regularization term is carefully designed, relying on a decomposition of the scene on a set of dictionaries. The dictionary atoms and the supports of the corresponding active coding coefficients are learned from the observed images. Then, conditionally on these dictionaries and supports, the fusion problem is solved via alternating optimization with respect to the target image (using the alternating direction method of multipliers) and the coding coefficients. Simulation results demonstrate the efficiency of the proposed algorithm when compared with state-of-the-art fusion methods. Numéro de notice : A2015-315 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2381272 En ligne : https://doi.org/10.1109/TGRS.2014.2381272 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76564
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 7 (July 2015) . - pp 3658 - 3668[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2015071 RAB Revue Centre de documentation En réserve L003 Disponible MTF-adjusted pansharpening approach based on coupled multiresolution decompositions / Abdelaziz Kallel in IEEE Transactions on geoscience and remote sensing, vol 53 n° 6 (June 2015)PermalinkCAESAR: an approach based on covariance matrix decomposition to improve multibaseline–multitemporal interferometric SAR processing / Gianfranco Fornaro in IEEE Transactions on geoscience and remote sensing, vol 53 n° 4 (April 2015)PermalinkPolarimetric incoherent target decomposition by means of independent component analysis / Nikola Besic in IEEE Transactions on geoscience and remote sensing, vol 53 n° 3 (March 2015)PermalinkFully polarimetric synthetic aperture radar (SAR) processing for crop type identification / Gang Hong in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 2 (February 2015)PermalinkAn improved PolSAR image speckle reduction algorithm based on structural judgment and hybrid four-component polarimetric decomposition / Zegang Ding in IEEE Transactions on geoscience and remote sensing, vol 51 n° 8 (August 2013)PermalinkSoil moisture estimation under low vegetation cover using a multi-angular polarimetric decomposition / Thomas Jaghuber in IEEE Transactions on geoscience and remote sensing, vol 51 n° 4 Tome 2 (April 2013)PermalinkApports des données ALOS PALSAR polarimétriques à la détection des zones humides littorales (Sassandra, Côte d'Ivoire) / Kouakou Hervé Kouassi in Photo interprétation, European journal of applied remote sensing, vol 48 n° 4 (décembre 2012)PermalinkCharacterization of forests and deforestation in Cambodia using ALOS/PALSAR observation / R. Avtar in Geocarto international, vol 27 n° 2 (March 2012)PermalinkEvaluation of modified four-component scattering power decomposition method over highly rugged glaciated terrain / G. Singh in Geocarto international, vol 27 n° 2 (March 2012)PermalinkThe effect of orientation angle compensation on coherency matrix and polarimetric target decompositions / Jong-Sen Lee in IEEE Transactions on geoscience and remote sensing, vol 49 n° 1 Tome 1 (January 2011)Permalink