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Bagging and boosting ensemble classifiers for classification of multispectral, hyperspectral and PolSAR data: A comparative evaluation / Hamid Jafarzadeh in Remote sensing, vol 13 n° 21 (November-1 2021)
[article]
Titre : Bagging and boosting ensemble classifiers for classification of multispectral, hyperspectral and PolSAR data: A comparative evaluation Type de document : Article/Communication Auteurs : Hamid Jafarzadeh, Auteur ; Masoud Mahdianpari, Auteur ; Eric Gill, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 4405 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] analyse comparative
[Termes IGN] apprentissage automatique
[Termes IGN] arbre de décision
[Termes IGN] boosting adapté
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données polarimétriques
[Termes IGN] ensachage
[Termes IGN] Extreme Gradient Machine
[Termes IGN] image hyperspectrale
[Termes IGN] image multibande
[Termes IGN] image radar moirée
[Termes IGN] image ROSISRésumé : (auteur) In recent years, several powerful machine learning (ML) algorithms have been developed for image classification, especially those based on ensemble learning (EL). In particular, Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) methods have attracted researchers’ attention in data science due to their superior results compared to other commonly used ML algorithms. Despite their popularity within the computer science community, they have not yet been well examined in detail in the field of Earth Observation (EO) for satellite image classification. As such, this study investigates the capability of different EL algorithms, generally known as bagging and boosting algorithms, including Adaptive Boosting (AdaBoost), Gradient Boosting Machine (GBM), XGBoost, LightGBM, and Random Forest (RF), for the classification of Remote Sensing (RS) data. In particular, different classification scenarios were designed to compare the performance of these algorithms on three different types of RS data, namely high-resolution multispectral, hyperspectral, and Polarimetric Synthetic Aperture Radar (PolSAR) data. Moreover, the Decision Tree (DT) single classifier, as a base classifier, is considered to evaluate the classification’s accuracy. The experimental results demonstrated that the RF and XGBoost methods for the multispectral image, the LightGBM and XGBoost methods for hyperspectral data, and the XGBoost and RF algorithms for PolSAR data produced higher classification accuracies compared to other ML techniques. This demonstrates the great capability of the XGBoost method for the classification of different types of RS data. Numéro de notice : A2021-823 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs13214405 Date de publication en ligne : 02/11/2021 En ligne : https://doi.org/10.3390/rs13214405 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98938
in Remote sensing > vol 13 n° 21 (November-1 2021) . - n° 4405[article]PolSAR ship detection based on neighborhood polarimetric covariance matrix / Tao Liu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 6 (June 2021)
[article]
Titre : PolSAR ship detection based on neighborhood polarimetric covariance matrix Type de document : Article/Communication Auteurs : Tao Liu, Auteur ; Ziyuan Yang, Auteur ; Armando Marino, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 4874 - 4887 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] détection d'objet
[Termes IGN] données polarimétriques
[Termes IGN] image radar moirée
[Termes IGN] image Radarsat
[Termes IGN] matrice de covariance
[Termes IGN] navire
[Termes IGN] polarimétrie radar
[Termes IGN] voisinage (relation topologique)Résumé : (auteur) The detection of small ships in polarimetric synthetic aperture radar (PolSAR) images is still a topic for further investigation. Recently, patch detection techniques, such as superpixel-level detection, have stimulated wide interest because they can use the information contained in similarities among neighboring pixels. In this article, we propose a novel neighborhood polarimetric covariance matrix (NPCM) to detect the small ships in PolSAR images, leading to a significant improvement in the separability between ship targets and sea clutter. The NPCM utilizes the spatial correlation between neighborhood pixels and maps the representation for a given pixel into a high-dimensional covariance matrix by embedding spatial and polarization information. Using the NPCM formalism, we apply a standard whitening filter, similar to the polarimetric whitening filter (PWF). We show how the inclusion of neighborhood information improves the performance compared with the traditional polarimetric covariance matrix. However, this is at the expense of a higher computation cost. The theory is validated via the simulated and measured data under different sea states and using different radar platforms. Numéro de notice : A2021-424 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3022181 Date de publication en ligne : 22/09/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3018638 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97780
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 6 (June 2021) . - pp 4874 - 4887[article]Inversion of solar-induced chlorophyll fluorescence using polarization measurements of vegetation / Haiyan Yao in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 5 (May 2021)
[article]
Titre : Inversion of solar-induced chlorophyll fluorescence using polarization measurements of vegetation Type de document : Article/Communication Auteurs : Haiyan Yao, Auteur ; Ziying Li, Auteur ; Yang Han, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 331-338 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] chlorophylle
[Termes IGN] couvert végétal
[Termes IGN] données polarimétriques
[Termes IGN] fluorescence
[Termes IGN] polarisationRésumé : (Auteur) In vegetation remote sensing, the apparent radiation of the vegetation canopy is often combined with three components derived from different parts of vegetation that have different production mechanisms and optical properties: volume scattering Lvol, polarized light Lpol, and chlorophyll fluorescence ChlF. The chlorophyll fluorescence plays a very important role in vegetation remote sensing, and the polarization information in vegetation remote sensing has become an effective way to characterize the physical characteristics of vegetation. This study analyzes the difference between these three types of radiation flux and utilizes polarization radiation to separate them from the apparent radiation of the vegetation canopy. Specifically, solar-induced chlorophyll fluorescence is extracted from vegetation canopy radiation data using standard Fraunhofer-line discrimination. The results show that polarization measurements can quantitatively separate Lvol, Lpol, and ChlF and extract the solar-induced chlorophyll fluorescence. This study improves our understanding of the light-scattering properties of vegetation canopies and provides insights for developing building models and research algorithms. Numéro de notice : A2021-365 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.5.331 Date de publication en ligne : 01/05/2021 En ligne : https://doi.org/10.14358/PERS.87.5.331 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97694
in Photogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 5 (May 2021) . - pp 331-338[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 105-2021051 SL Revue Centre de documentation Revues en salle Disponible On the polarimetric variable improvement via alignment of subarray channels in PPAR using weather returns / Igor R. Ivić in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 3 (March 2021)
[article]
Titre : On the polarimetric variable improvement via alignment of subarray channels in PPAR using weather returns Type de document : Article/Communication Auteurs : Igor R. Ivić, Auteur Année de publication : 2021 Article en page(s) : pp 2015 - 2027 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal
[Termes IGN] antenne radar
[Termes IGN] coefficient de corrélation
[Termes IGN] données météorologiques
[Termes IGN] données polarimétriques
[Termes IGN] écho radar
[Termes IGN] faisceau
[Termes IGN] mesurage de phase
[Termes IGN] oscillateur
[Termes IGN] polarimétrie radar
[Termes IGN] variance de phaseRésumé : (Auteur) Many modern phased-array radars (PARs) are multichannel systems that include multiple receivers for data acquisition. Each channel provides a signal from a group of Transmit/Receive modules comprising a section of the antenna. Channels typically consist of a full receive path, often with an independent local oscillator (LO) clock source. Such arrangement provides for beamforming flexibility on receive which can be applied in a digital domain. Consequently, the channel-to-channel phase and magnitude alignment is critical to maximizing the performance of the digital beamforming process and the accuracy of resulting detections and measurements. Herein, a novel method to improve such alignment using weather returns and achieve the improvement in the polarimetric variable estimates is described. Numéro de notice : A2021-213 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3003293 Date de publication en ligne : 10/07/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3003293 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97201
in IEEE Transactions on geoscience and remote sensing > Vol 59 n° 3 (March 2021) . - pp 2015 - 2027[article]Tropical forest canopy height estimation from combined polarimetric SAR and LiDAR using machine-learning / Maryam Pourshamsi in ISPRS Journal of photogrammetry and remote sensing, vol 172 (February 2021)
[article]
Titre : Tropical forest canopy height estimation from combined polarimetric SAR and LiDAR using machine-learning Type de document : Article/Communication Auteurs : Maryam Pourshamsi, Auteur ; Junshi Xia, Auteur ; Naoto Yokoya, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 79 - 94 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] bande L
[Termes IGN] canopée
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] données lidar
[Termes IGN] données polarimétriques
[Termes IGN] forêt tropicale
[Termes IGN] Gabon
[Termes IGN] hauteur des arbres
[Termes IGN] image captée par drone
[Termes IGN] image radar moirée
[Termes IGN] Rotation Forest classification
[Termes IGN] semis de pointsRésumé : (auteur) Forest height is an important forest biophysical parameter which is used to derive important information about forest ecosystems, such as forest above ground biomass. In this paper, the potential of combining Polarimetric Synthetic Aperture Radar (PolSAR) variables with LiDAR measurements for forest height estimation is investigated. This will be conducted using different machine learning algorithms including Random Forest (RFs), Rotation Forest (RoFs), Canonical Correlation Forest (CCFs) and Support Vector Machine (SVMs). Various PolSAR parameters are required as input variables to ensure a successful height retrieval across different forest heights ranges. The algorithms are trained with 5000 LiDAR samples (less than 1% of the full scene) and different polarimetric variables. To examine the dependency of the algorithm on input training samples, three different subsets are identified which each includes different features: subset 1 is quiet diverse and includes non-vegetated region, short/sparse vegetation (0–20 m), vegetation with mid-range height (20–40 m) to tall/dense ones (40–60 m); subset 2 covers mostly the dense vegetated area with height ranges 40–60 m; and subset 3 mostly covers the non-vegetated to short/sparse vegetation (0–20 m) .The trained algorithms were used to estimate the height for the areas outside the identified subset. The results were validated with independent samples of LiDAR-derived height showing high accuracy (with the average R2 = 0.70 and RMSE = 10 m between all the algorithms and different training samples). The results confirm that it is possible to estimate forest canopy height using PolSAR parameters together with a small coverage of LiDAR height as training data. Numéro de notice : A2021-086 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.11.008 Date de publication en ligne : 19/12/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.11.008 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96846
in ISPRS Journal of photogrammetry and remote sensing > vol 172 (February 2021) . - pp 79 - 94[article]Exemplaires(2)
Code-barres Cote Support Localisation Section Disponibilité 081-2021021 SL Revue Centre de documentation Revues en salle Disponible 081-2021022 DEP-RECF Revue Nancy Bibliothèque Nancy IFN Exclu du prêt Evaluation of Sentinel-1 & 2 time series for the identification and characterization of ecological continuities, from wooded to crop-dominated landscapes / Audrey Mercier (2021)PermalinkQuantification probabiliste des taux de déformation crustale par inversion bayésienne de données GPS / Colin Pagani (2021)PermalinkReal-time multimodal semantic scene understanding for autonomous UGV navigation / Yifei Zhang (2021)PermalinkMonitoring of wheat crops using the backscattering coefficient and the interferometric coherence derived from Sentinel-1 in semi-arid areas / Nadia Ouaadi in Remote sensing of environment, Vol 251 (15 December 2020)PermalinkL-band SAR for estimating aboveground biomass of rubber plantation in Java Island, Indonesia / Bambang H Trisasongko in Geocarto international, vol 35 n° 12 ([01/09/2020])PermalinkPolarimetric SAR calibration and residual error estimation when corner reflectors are unavailable / Lei Shi in IEEE Transactions on geoscience and remote sensing, vol 58 n° 6 (June 2020)PermalinkFusing adjacent-track InSAR datasets to densify the temporal resolution of time-series 3-D displacement estimation over mining areas with a prior deformation model and a generalized weighting least-squares method / Yuedong Wang in Journal of geodesy, vol 94 n° 5 (May 2020)PermalinkIdentification of alpine glaciers in the central Himalayas using fully polarimetric L-Band SAR data / Guo-Hui Yao in IEEE Transactions on geoscience and remote sensing, vol 58 n° 1 (January 2020)PermalinkInversion de données PolSAR en bande P pour l'estimation de la biomasse forestière / Colette Gelas (2020)PermalinkPolarization dependence of azimuth cutoff from quad-pol SAR images / Huimin Li in IEEE Transactions on geoscience and remote sensing, vol 57 n° 12 (December 2019)PermalinkSoil and vegetation scattering contributions in L-Band and P-Band polarimetric SAR observations / S. Hamed Alemohammad in IEEE Transactions on geoscience and remote sensing, vol 57 n° 11 (November 2019)PermalinkThe cause of the 2011 Hawthorne (Nevada) earthquake swarm constrained by seismic and InSAR methods / Xianjie Zha in Journal of geodesy, vol 93 n°6 (June 2019)PermalinkUsing Sentinel-1A DInSAR interferometry and Landsat 8 data for monitoring water level changes in two lakes in Crete, Greece / D.D. Alexakis in Geocarto international, vol 34 n° 7 ([01/06/2019])PermalinkDeveloping a subswath-based wind speed retrieval model for sentinel-1 VH-Polarized SAR data over the ocean surface / Kangyu Zhang in IEEE Transactions on geoscience and remote sensing, vol 57 n° 3 (March 2019)PermalinkEvaluation of time-series SAR and optical images for the study of winter land-use / Julien Denize (2019)PermalinkLong-term land deformation monitoring using quasi-persistent scatterer (Q-PS) technique observed by sentinel-1A : case study Kelok Sembilan / Pakhrur Razi in Advances in Remote Sensing, vol 7 n° 4 (December 2018)PermalinkPolarization orientation angle and polarimetric SAR scattering characteristics of steep terrain / Jong-Sen Lee in IEEE Transactions on geoscience and remote sensing, vol 56 n° 12 (December 2018)PermalinkPotential of Sentinel-1 data for monitoring temperate mixed forest phenology / Pierre-Louis Frison in Remote sensing, vol 10 n° 12 (December 2018)PermalinkSeparating the influence of vegetation changes in polarimetric differential SAR interferometry / Virginia Brancato in IEEE Transactions on geoscience and remote sensing, vol 56 n° 12 (December 2018)PermalinkUnmixing polarimetric radar images based on land cover type identified by higher resolution optical data before target decomposition: application to forest and bare soil / Sébastien Giordano in IEEE Transactions on geoscience and remote sensing, vol 56 n° 10 (October 2018)PermalinkEstimation of winter wheat crop growth parameters using time series Sentinel-1A SAR data / P. Kumar in Geocarto international, vol 33 n° 9 (September 2018)PermalinkComplex-valued convolutional neural network and its application in polarimetric SAR image classification / Zhimian Zhang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 12 (December 2017)PermalinkIncidence angle dependence of first-year sea ice backscattering coefficient in Sentinel-1 SAR Imagery over the kara sea / Marko P. Mäkynen in IEEE Transactions on geoscience and remote sensing, vol 55 n° 11 (November 2017)PermalinkAn information fusion approach for PALSAR data to retrieve soil moisture / Ankita Jain in Geocarto international, vol 32 n° 9 (September 2017)PermalinkCritical analysis of model-based incoherent polarimetric decomposition methods and investigation of deorientation effect / Pooja Mishra in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)PermalinkDistance measure based change detectors for polarimetric SAR imagery / Yonghong Zhang in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 9 (September 2016)PermalinkThe impacts of building orientation on polarimetric orientation angle estimation and model-based decomposition for multilook polarimetric SAR data in urban areas / Hongzhong Li in IEEE Transactions on geoscience and remote sensing, vol 54 n° 9 (September 2016)PermalinkSoil 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)PermalinkGLORI: A GNSS-R Dual Polarization Airborne Instrument for Land Surface Monitoring / Erwan Motte in Sensors, vol 16 n° 5 (May 2016)PermalinkCompressive 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)PermalinkFirst results from the GLORIE polarimetric GNSS-R airborne campaign dedicated to land parameters estimation / Erwan Motte (2016)PermalinkRadar based classification prior to biomass retrieval from P-Band SAR data / Pierre-Louis Frison (2016)PermalinkCorrecting distortion of polarimetric SAR data induced by ionospheric scintillation / Jun Su Kim in IEEE Transactions on geoscience and remote sensing, vol 53 n° 12 (December 2015)PermalinkForest height estimation by means of Pol-InSAR data inversion : The role of the vertical wavenumber / Florian Kugler in IEEE Transactions on geoscience and remote sensing, vol 53 n° 10 (October 2015)PermalinkTerraSAR-X dual-pol time-series for mapping of wetland vegetation / Julie Betbeder in ISPRS Journal of photogrammetry and remote sensing, vol 107 (September 2015)PermalinkRandom Forest and Rotation Forest for fully polarized SAR image classification using polarimetric and spatial features / Peijun Du in ISPRS Journal of photogrammetry and remote sensing, vol 105 (July 2015)PermalinkCompilation de données radar et optiques pour la cartographie des classes d'occupation du sol aux environs du système lacustre de Bizerte (Tunisie du Nord) / Ibtissem Amri in Photo interprétation, European journal of applied remote sensing, vol 51 n° 2 (juin 2015)PermalinkA multidimensional extension of the concept of coherence in polarimetric SAR interferometry / Jose Luis Alvarez-Perez in IEEE Transactions on geoscience and remote sensing, vol 53 n° 3 (March 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)PermalinkPolarimetric SAR speckle filtering and the extended sigma filter / Jong-Sen Lee in IEEE Transactions on geoscience and remote sensing, vol 53 n° 3 (March 2015)PermalinkCalibration of SAR polarimetric images by means of a covariance matching approach / Alberto Villa in IEEE Transactions on geoscience and remote sensing, vol 53 n° 2 (February 2015)PermalinkMultibaseline polarimetric synthetic aperture radar tomography of forested areas using wavelet-based distribution compressive sensing / Lei Liang in Journal of applied remote sensing, vol 9 (2015)PermalinkRelating statistical characteristics of cross-polarized phase difference to speckle noise / Huimin Li in Journal of applied remote sensing, vol 9 (2015)PermalinkAssessment of the relevance of information derived from the unmixing of polarimetric radar images / Sébastien Giordano (2015)PermalinkDémélange d’images radar polarimétrique par séparation thématique de sources / Sébastien Giordano (2015)PermalinkLand cover and soil type mapping from spaceborne PolSAR Data at L-Band with probabilistic neural network / Oleg Antropov in IEEE Transactions on geoscience and remote sensing, vol 52 n° 9 Tome 1 (September 2014)PermalinkVegetation height estimation precision with compact PolInSAR and homogeneous random volume over ground model / Aurélien Arnaubec in IEEE Transactions on geoscience and remote sensing, vol 52 n° 3 (March 2014)PermalinkStudy on the polarimetric characteristics of the Lop Nur arid area using PolSAR data / Zhihong Gao in Journal of applied remote sensing, vol 8 (2014)PermalinkPermalinkOrthorectification of full-polarimetric radarsat-2 data using accurate LIDAR DSM / Thierry Toutin in IEEE Transactions on geoscience and remote sensing, vol 51 n° 12 (December 2013)PermalinkWavelet-Based Compressed Sensing for SAR Tomography of Forested Areas / Esteban Aguilera in IEEE Transactions on geoscience and remote sensing, vol 51 n° 12 (December 2013)PermalinkEstimation of glacier ice extinction using long-wavelength airborne Pol-InSAR / Jayanti J. Sharma in IEEE Transactions on geoscience and remote sensing, vol 51 n° 6 Tome 2 (June 2013)PermalinkDual-Polarimetric signatures of vegetation – a case study Biebrza / Dariusz Ziolkowski in Geoinformation issues, vol 5 n° 1 (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)PermalinkA proposed framework to unmix scattering mechanisms of polarimetric radar images using very high resolution optical images / Sébastien Giordano in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol I-7 (2012)PermalinkModeling and simulation of polarimetric hyperspectral imaging process / Junping Zhang in IEEE Transactions on geoscience and remote sensing, vol 50 n° 6 (June 2012)PermalinkFiltering and segmentation of polarimetric SAR data based on binary partition trees / A. Alonso-Gonzalez in IEEE Transactions on geoscience and remote sensing, vol 50 n° 2 (February 2012)PermalinkMarkov Chain CFAR detection for polarimetric data using data fusion / C. Fei in IEEE Transactions on geoscience and remote sensing, vol 50 n° 2 (February 2012)PermalinkCharacterization of Arctic sea ice thickness using high-resolution spaceborne polarimetric SAR data / J.W. Kim in IEEE Transactions on geoscience and remote sensing, vol 50 n° 1 (January 2012)PermalinkImproving the Wishart synthetic aperture radar image classifications through deterministic simulated annealing / F. Sanchez-Llado in ISPRS Journal of photogrammetry and remote sensing, vol 66 n° 6 (November 2011)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)PermalinkStatus and future of laser scanning, synthetic aperture radar and hyperspectral remote sensing data for forest biomass assessment / B. Koch in ISPRS Journal of photogrammetry and remote sensing, vol 65 n° 6 (November - December 2010)PermalinkPrediction, detection and correction of Faraday: rotation in full-polarimetric L-Band SAR data / F.J. Meyer in IEEE Transactions on geoscience and remote sensing, vol 46 n° 10 Tome 2 (October 2008)PermalinkThe compact polarimetry alternative for spaceborne SAR at low frequency / P. Dubois-Fernandez in IEEE Transactions on geoscience and remote sensing, vol 46 n° 10 Tome 2 (October 2008)PermalinkEstuarine shoreline change detection using Japanese ALOS PALSAR HH and JERS-1 L-HH SAR data in the Albemarle-Pamlico Sounds, North Carolina, USA / Y. Wang in International Journal of Remote Sensing IJRS, vol 29 n° 15-16 (August 2008)PermalinkApplications of ICA for the enhancement and classification of polarimetric SAR images / H. Wang in International Journal of Remote Sensing IJRS, vol 29 n° 6 (March 2008)PermalinkRetrieval of surface roughness using multi-polarized Envisat-1 ASAR data / H.s Srivastava in Geocarto international, vol 23 n° 1 (February - March 2008)PermalinkEvaluation de la classification WISHART sur des données radar polarimétriques et application au Gabon / G. Roussel (2008)PermalinkA new application for PolSAR imagery in the field of moving target indication/ship detection / C. Liu in IEEE Transactions on geoscience and remote sensing, vol 45 n° 11 Tome 1 (November 2007)PermalinkComparison of L- and X-band PolSAR data for characterization of polarization orientation angle shift induced by man-made structure / K. Iribe (2007)PermalinkClassification of fully polarimetric SAR data for land use cartography / Cédric Lardeux in Revue Française de Photogrammétrie et de Télédétection, n° 182 (Juin 2006)PermalinkA three dimensional scatterer map extends the DEM information in urban areas / Nico Adam in Revue Française de Photogrammétrie et de Télédétection, n° 182 (Juin 2006)PermalinkElaboration de MNT par extraction de pentes topographiques de données polarimétriques / Yohann Ly (2006)Permalink