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Burn severity analysis in Mediterranean forests using maximum entropy model trained with EO-1 Hyperion and LiDAR data / Alfonso Fernández-Manso in ISPRS Journal of photogrammetry and remote sensing, vol 155 (September 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 155 (September 2019) . - pp 102 - 118 Titre : Burn severity analysis in Mediterranean forests using maximum entropy model trained with EO-1 Hyperion and LiDAR data Type de document : Article/Communication Auteurs : Alfonso Fernández-Manso, Auteur ; Carmen Quintano, Auteur ; Dar A. Roberts, Auteur Année de publication : 2019 Article en page(s) : pp 102 - 118 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] entropie [Termes descripteurs IGN] forêt méditerranéenne [Termes descripteurs IGN] image EO1-Hyperion [Termes descripteurs IGN] incendie de forêt [Termes descripteurs IGN] multiple endmember spectral mixture analysis Résumé : (Auteur) All ecosystems and in particular ecosystems in Mediterranean climates are affected by fires. Knowledge of the drivers that most influence burn severity patterns as well an accurate map of post-fire effects are key tools for forest managers in order to plan an adequate post-fire response. Remote sensing data are becoming an indispensable instrument to reach both objectives. This work explores the relative influence of pre-fire vegetation structure and topography on burn severity compared to the impact of post-fire damage level, and evaluates the utility of the Maximum Entropy (MaxEnt) classifier trained with post-fire EO-1 Hyperion data and pre-fire LiDAR to model three levels of burn severity at high accuracy. We analyzed a large fire in central-eastern Spain, which occurred on 16–19 June 2016 in a maquis shrubland and Pinus halepensis forested area. Post-fire hyperspectral Hyperion data were unmixed using Multiple Endmember Spectral Mixture Analysis (MESMA) and five fraction images were generated: char, green vegetation (GV), non-photosynthetic vegetation, soil (NPVS) and shade. Metrics associated with vegetation structure were calculated from pre-fire LiDAR. Post-fire MESMA char fraction image, pre-fire structural metrics and topographic variables acted as inputs to MaxEnt, which built a model and generated as output a suitability surface for each burn severity level. The percentage of contribution of the different biophysical variables to the MaxEnt model depended on the burn severity level (LiDAR-derived metrics had a greater contribution at the low burn severity level), but MaxEnt identified the char fraction image as the highest contributor to the model for all three burn severity levels. The present study demonstrates the validity of MaxEnt as one-class classifier to model burn severity accurately in Mediterranean countries, when trained with post-fire hyperspectral Hyperion data and pre-fire LiDAR. Numéro de notice : A2019-313 Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.isprsjprs.2019.07.003 date de publication en ligne : 14/07/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.07.003 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93339 [article]

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3D hyperspectral point cloud generation: Fusing airborne laser scanning and hyperspectral imaging sensors for improved object-based information extraction / Maximilian Brell in ISPRS Journal of photogrammetry and remote sensing, vol 149 (March 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 149 (March 2019) . - pp 200 - 214 Titre : 3D hyperspectral point cloud generation: Fusing airborne laser scanning and hyperspectral imaging sensors for improved object-based information extraction Type de document : Article/Communication Auteurs : Maximilian Brell, Auteur ; Karl Segl, Auteur ; Luis Guanter, Auteur ; Bodo Bookhagen, Auteur Année de publication : 2019 Article en page(s) : pp 200 - 214 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes descripteurs IGN] analyse des mélanges spectraux [Termes descripteurs IGN] capteur hyperspectral [Termes descripteurs IGN] classification orientée objet [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] étiquetage sémantique [Termes descripteurs IGN] fusion de données [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] impulsion laser [Termes descripteurs IGN] niveau de détail [Termes descripteurs IGN] segmentation d'image [Termes descripteurs IGN] semis de points Résumé : (Auteur) Remote Sensing technologies allow to map biophysical, biochemical, and earth surface parameters of the land surface. Of especial interest for various applications in environmental and urban sciences is the combination of spectral and 3D elevation information. However, those two data streams are provided separately by different instruments, namely airborne laser scanner (ALS) for elevation and a hyperspectral imager (HSI) for high spectral resolution data. The fusion of ALS and HSI data can thus lead to a single data entity consistently featuring rich structural and spectral information. In this study, we present the application of fusing the first pulse return information from ALS data at a sub-decimeter spatial resolution with the lower-spatial resolution hyperspectral information available from the HSI into a hyperspectral point cloud (HSPC). During the processing, a plausible hyperspectral spectrum is assigned to every first-return ALS point. We show that the complementary implementation of spectral and 3D information at the point-cloud scale improves object-based classification and information extraction schemes. This improvements have great potential for numerous land-cover mapping and environmental applications. Numéro de notice : A2019-119 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.01.022 date de publication en ligne : 06/02/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.01.022 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92448 [article]

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Spectral unmixing with perturbed endmembers / Reza Arablouei in IEEE Transactions on geoscience and remote sensing, vol 57 n° 1 (January 2019)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 57 n° 1 (January 2019) . - pp 194 - 211 Titre : Spectral unmixing with perturbed endmembers Type de document : Article/Communication Auteurs : Reza Arablouei, Auteur Année de publication : 2019 Article en page(s) : pp 194 - 211 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse des mélanges spectraux [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] matrice d'information de Fischer Résumé : (Auteur) We consider the problem of supervised spectral unmixing with a fully-perturbed linear mixture model where the given endmembers, as well as the observations of the spectral image, are subject to perturbation due to noise, error, or model mismatch. We calculate the Fisher information matrix and the Cramer-Rao lower bound associated with the estimation of the abundance matrix in the considered fully-perturbed linear spectral unmixing problem. We develop an algorithm for estimating the abundance matrix by minimizing a constrained and regularized maximum-log-likelihood objective function using the block coordinate-descend iterations and the alternating direction method of multipliers. We analyze the convergence of the proposed algorithm theoretically and perform simulations with real hyperspectral image data sets to evaluate its performance. The simulation results corroborate the efficacy of the proposed algorithm in mitigating the adverse effects of perturbation in the endmembers. Numéro de notice : A2019-105 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2018.2852745 date de publication en ligne : 26/07/2018 En ligne : https://doi.org/10.1109/TGRS.2018.2852745 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92411 [article]

Urban impervious surface estimation from remote sensing and social data / Yan Yu in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 12 (December 2018)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 84 n° 12 (December 2018) . - pp 771 - 780 Titre : Urban impervious surface estimation from remote sensing and social data Type de document : Article/Communication Auteurs : Yan Yu, Auteur ; Jun Li, Auteur ; Changyu Zhu, Auteur ; Antonio J. Plaza, Auteur Année de publication : 2018 Article en page(s) : pp 771 - 780 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes descripteurs IGN] analyse des mélanges spectraux [Termes descripteurs IGN] base de données routières [Termes descripteurs IGN] Canton (Kouangtoung) [Termes descripteurs IGN] contenu généré par les utilisateurs [Termes descripteurs IGN] données issues des réseaux sociaux [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] données vectorielles [Termes descripteurs IGN] Google Maps [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] image Landsat-OLI [Termes descripteurs IGN] OpenStreetMap [Termes descripteurs IGN] point d'intérêt [Termes descripteurs IGN] régression multiple [Termes descripteurs IGN] réseau routier [Termes descripteurs IGN] surface imperméable [Termes descripteurs IGN] zone urbaine Résumé : (auteur) We propose an inspiring approach for accurate impervious surface estimation based on the integration of remote sensing and social data. The proposed approach exploits the strengths of two kind of heterogeneous features, i.e., physical features and social features, where the former ones are derived by a morphological attribute profiles-guided spectral mixture analysis model using remote sensing imagery, and the latter ones are obtained from the normalized kernel density of point of interest and vector road datasets. These two features are then integrated using a multivariable linear regression model to estimate impervious surfaces. The proposed method has been tested in the main urban area of Guangzhou, China, in pixel level and parcel level, respectively. The obtained results, with the overall RMSE of 10.98% and 10.90% for pixel level and parcel level, respectively, demonstrate the good performance of integrating remote sensing imagery and social data for mapping of urban impervious surface. Numéro de notice : A2018-549 Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.84.12.771 date de publication en ligne : 01/12/2018 En ligne : https://doi.org/10.14358/PERS.84.12.771 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91622 [article]

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Unmixing 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)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 56 n° 10 (October 2018) . - 13 p. Titre : Unmixing polarimetric radar images based on land cover type identified by higher resolution optical data before target decomposition: application to forest and bare soil Type de document : Article/Communication Auteurs : Sébastien Giordano , Auteur ; Grégoire Mercier, Auteur ; Jean-Paul Rudant , Auteur Année de publication : 2018 Projets : 1-Pas de projet / Article en page(s) : 13 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] analyse des mélanges spectraux [Termes descripteurs IGN] analyse linéaire des mélanges spectraux [Termes descripteurs IGN] biomasse aérienne [Termes descripteurs IGN] décomposition spectrale [Termes descripteurs IGN] données polarimétriques [Termes descripteurs IGN] forêt [Termes descripteurs IGN] image Radarsat [Termes descripteurs IGN] matrice de covariance [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] polarimétrie radar [Termes descripteurs IGN] sol nu [Termes descripteurs IGN] surface forestière Résumé : (auteur) Extracting information from a polarimetric radar representation usually consists in decomposing it with target decomposition algorithms. This first step can be seen as a geometric analysis of the polarimetric information: the identification of physical radar scattering mechanisms. The problem is that average physical parameters are estimated. As a consequence, these parameters might not describe correctly any of the land cover types that can be mixed together into the radar resolution cell. Therefore, using the polarimetric parameters for land cover classification is challenging. The novelty of the method is to propose a thematic analysis of the polarimetric information preceding the geometric one. The objective is to assess if splitting off polarimetric information on a land cover type basis before applying usual target decomposition algorithms can produce more consistent radar scattering mechanisms when land cover classes are mixed inside the radar resolution cell. A cooperative fusion framework in which very high-resolution optical images are used to unmix physical radar scattering mechanisms is proposed. For bare soil and forests, we point out that a linear unmixing model applied to the covariance matrix is able to split off polarimetric information on a land cover type basis. The assessment of the unmixed radar matrices is carried out with polarimetric radar images from the Radarsat-2 satellite. It was found that despite speckle, the reconstructed radar information after the unmixing process is statistically relevant with the observations. The question whether the unmixed radar images contain relevant thematic information is more challenging, but results tend to validate this property. This method could be used to have a better estimation of vegetation biomass in the context of open forested areas. Numéro de notice : A2018-331 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2018.2827258 date de publication en ligne : 09/07/2018 En ligne : https://doi.org/10.1109/TGRS.2018.2827258 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90475 [article]

Predicting temperate forest stand types using only structural profiles from discrete return airborne lidar / Melissa Fedrigo in ISPRS Journal of photogrammetry and remote sensing, vol 136 (February 2018)  

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Detection and area estimation for photovoltaic panels in urban hyperspectral remote sensing data by an original NMF-based unmixing method / Moussa Sofiane Karoui (2018)  

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Robust minimum volume simplex analysis for hyperspectral unmixing / Shaoquan Zhang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 11 (November 2017)  

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Sparse distributed multitemporal hyperspectral unmixing / Jakob Sigurdsson in IEEE Transactions on geoscience and remote sensing, vol 55 n° 11 (November 2017)  

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Spatial group sparsity regularized nonnegative matrix factorization for hyperspectral unmixing / Xinyu Wang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 11 (November 2017)  

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From subpixel to superpixel : a novel fusion framework for hyperspectral image classification / Ting Lu in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)  

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A novel preunmixing framework for efficient detection of linear mixtures in hyperspectral images / Andrea Marinoni in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)  

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Joint hyperspectral superresolution and unmixing with interactive feedback / Chen Yi in IEEE Transactions on geoscience and remote sensing, vol 55 n° 7 (July 2017)  

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Total variation regularized reweighted sparse nonnegative matrix factorization for hyperspectral unmixing / Wei He in IEEE Transactions on geoscience and remote sensing, vol 55 n° 7 (July 2017)  

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Multilayer NMF for blind unmixing of hyperspectral imagery with additional constraints / L. Chen in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 4 (April 2017)  

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