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Pansharpening: context-based generalized Laplacian pyramids by robust regression / Gemine Vivone in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 9 (September 2020) . - pp 6152 - 6167 Titre : Pansharpening: context-based generalized Laplacian pyramids by robust regression Type de document : Article/Communication Auteurs : Gemine Vivone, Auteur ; Stefano Marano, Auteur ; Jocelyn Chanussot, Auteur Année de publication : 2020 Article en page(s) : pp 6152 - 6167 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse multirésolution [Termes descripteurs IGN] fonction de transfert de modulation [Termes descripteurs IGN] fusion d'images [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] image panchromatique [Termes descripteurs IGN] lissage de données [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] pansharpening (fusion d'images) [Termes descripteurs IGN] régression [Termes descripteurs IGN] transformation en ondelettes Résumé : (auteur) Pansharpening refers to the combination of panchromatic (PAN) and multispectral (MS) images, designed to obtain a fused product retaining the fine spatial resolution of the former and the high spectral content of the latter. One of the most popular and successful approaches to pansharpening is the method known as context-based generalized Laplacian pyramid, which requires as a key ingredient for the estimation of the so-called injection coefficients. In this article, we propose the adoption of robust techniques for the estimation of the injection coefficients and detection strategies to select the clusters for which robust regression is needed, providing a suitable balancing between fusion performance and computational burden. Experimental results conducted on five real data sets acquired by the sensors QuickBird, WorldView-3, and WorldView-4, show the superiority of the proposed method with respect to current state-of-the-art pansharpening techniques. Numéro de notice : A2020-528 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2974806 date de publication en ligne : 04/03/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2974806 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95706 [article]

Tree species classification in tropical forests using visible to shortwave infrared WorldView-3 images and texture analysis / Matheus Pinheiro Ferreira 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 119 - 131 Titre : Tree species classification in tropical forests using visible to shortwave infrared WorldView-3 images and texture analysis Type de document : Article/Communication Auteurs : Matheus Pinheiro Ferreira, Auteur ; Fabien Hubert Wagner, Auteur ; Luiz E.O.C. Aragão, Auteur Année de publication : 2019 Article en page(s) : pp 119 - 131 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse texturale [Termes descripteurs IGN] arbre (flore) [Termes descripteurs IGN] Brésil [Termes descripteurs IGN] canopée [Termes descripteurs IGN] classification dirigée [Termes descripteurs IGN] espèce végétale [Termes descripteurs IGN] forêt tropicale [Termes descripteurs IGN] houppier [Termes descripteurs IGN] image à très haute résolution [Termes descripteurs IGN] image infrarouge [Termes descripteurs IGN] image Worldview [Termes descripteurs IGN] inventaire forestier (techniques et méthodes) [Termes descripteurs IGN] matrice de co-occurence [Termes descripteurs IGN] pansharpening (fusion d'images) [Termes descripteurs IGN] variation saisonnière Résumé : (Auteur) Tropical forest conservation and management can significantly benefit from information about the spatial distribution of tree species. Very-high resolution (VHR) spaceborne platforms have been hailed as a promising technology for mapping tree species over broad spatial extents. WorldView-3, the most advanced VHR sensor, provides spectral data in 16 bands covering the visible to near-infrared (VNIR, 400–1040 nm) and shortwave-infrared (SWIR, 1210–2365 nm) wavelength ranges. It also collects images at unprecedented levels of details using a panchromatic band with 0.3-m of spatial resolution. However, the potential of WorldView-3 at its full spectral and spatial resolution for tropical tree species classification remains unknown. In this study, we performed a comprehensive assessment of WorldView-3 images acquired in the dry and wet seasons for tree species discrimination in tropical semi-deciduous forests. Classification experiments were performed using VNIR individually and combined with SWIR channels. To take advantage of the sub-metric resolution of the panchromatic band for classification, we applied an individual tree crown (ITC)-based approach that employed pan-sharpened VNIR bands and gray level co-occurrence matrix texture features. We determined whether the combination of images from the two annual seasons improves the classification accuracy. Finally, we investigated which plant traits influenced species detection. The new SWIR sensing capabilities of WorldView-3 increased the average producer’s accuracy up to 7.8%, by enabling the detection of non-photosynthetic vegetation within ITCs. The combination of VNIR bands from the two annual seasons did not improve the classification results when compared to the results obtained using images from each season individually. The use of VNIR bands at their original 1.2-m spatial resolution yielded average producer’s accuracies of 43.1 ± 3.1% and 38.8 ± 3% in the wet and dry seasons, respectively. The ITC-based approach improved the accuracy to 70 ± 8% in the wet and 68.4 ± 7.4% in the dry season. Texture analysis of the panchromatic band enabled the detection of species-specific differences in crown structure, which improved species detection. The use of texture analysis, pan-sharpening, and ITC delineation is a potential approach to perform tree species classification in tropical forests with WorldView-3 satellite images. Numéro de notice : A2019-117 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.01.019 date de publication en ligne : 28/01/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.01.019 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92444 [article]

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Synergetic efficiency of Lidar and WorldView-2 for 3D urban cartography in Northeast Mexico / Fabiola D. Yepez-Rincon in Geocarto international, vol 34 n° 2 ([01/02/2019])  

Public [article]  inGeocarto international > vol 34 n° 2 [01/02/2019] . - pp 164 - 178 Titre : Synergetic efficiency of Lidar and WorldView-2 for 3D urban cartography in Northeast Mexico Type de document : Article/Communication Auteurs : Fabiola D. Yepez-Rincon, Auteur ; Diego Fabian Lozano Garcia, Auteur Année de publication : 2019 Article en page(s) : pp 164 - 178 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes descripteurs IGN] analyse en composantes principales [Termes descripteurs IGN] bande infrarouge [Termes descripteurs IGN] carte d'occupation du sol [Termes descripteurs IGN] cartographie urbaine [Termes descripteurs IGN] croissance urbaine [Termes descripteurs IGN] croissance végétale [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] image Worldview [Termes descripteurs IGN] Mexico (Mexique) [Termes descripteurs IGN] pansharpening (fusion d'images) [Termes descripteurs IGN] zone urbaine Résumé : (auteur) Three-dimensional urban cartography is needed for city changes’ assessment. The variety of studies using 3D calculations of urban elements grows each year. Building and vegetation volumes are necessary to assess and understand spatio-temporal urban changeable environments. However, there are technical questions as to which method can improve 3D urban cartographic accuracy. The innovative part of this current study is the creation of a six-band hybrid obtained from LIDAR and WorldView2 synergy. Two different enhancement algorithms demonstrated the most important spectral features for the urban development and vegetation classes. Results indicated an improvement in accuracy by up to 21.3%, according to the Kappa coefficient. Both infra-red band and intensity band were the most significant, according to the principal components analysis. The synergy delimited classes and polygons, as well as the direct display of information regarding heights of elements and improving the extraction of roads, buildings and vegetation classes. Numéro de notice : A2019-220 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2017.1377774 date de publication en ligne : 21/09/2017 En ligne : https://doi.org/10.1080/10106049.2017.1377774 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92738 [article]

Super-resolution of Sentinel-2 images : Learning a globally applicable deep neural network / Charis Lanaras in ISPRS Journal of photogrammetry and remote sensing, vol 146 (December 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 146 (December 2018) . - pp 305 - 319 Titre : Super-resolution of Sentinel-2 images : Learning a globally applicable deep neural network Type de document : Article/Communication Auteurs : Charis Lanaras, Auteur ; José Bioucas-Dias, Auteur ; Silvano Galliani, Auteur ; Emmanuel Baltsavias, Auteur ; Konrad Schindler, Auteur Année de publication : 2018 Article en page(s) : pp 305 - 319 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] bande spectrale [Termes descripteurs IGN] échantillonnage de données [Termes descripteurs IGN] erreur moyenne quadratique [Termes descripteurs IGN] image à basse résolution [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] pansharpening (fusion d'images) [Termes descripteurs IGN] pas d'échantillonnage au sol [Termes descripteurs IGN] pouvoir de résolution spectrale [Termes descripteurs IGN] réseau neuronal convolutif Résumé : (Auteur) The Sentinel-2 satellite mission delivers multi-spectral imagery with 13 spectral bands, acquired at three different spatial resolutions. The aim of this research is to super-resolve the lower-resolution (20 m and 60 m Ground Sampling Distance – GSD) bands to 10 m GSD, so as to obtain a complete data cube at the maximal sensor resolution. We employ a state-of-the-art convolutional neural network (CNN) to perform end-to-end upsampling, which is trained with data at lower resolution, i.e., from 40  20 m, respectively 360  60 m GSD. In this way, one has access to a virtually infinite amount of training data, by downsampling real Sentinel-2 images. We use data sampled globally over a wide range of geographical locations, to obtain a network that generalises across different climate zones and land-cover types, and can super-resolve arbitrary Sentinel-2 images without the need of retraining. In quantitative evaluations (at lower scale, where ground truth is available), our network, which we call DSen2, outperforms the best competing approach by almost 50% in RMSE, while better preserving the spectral characteristics. It also delivers visually convincing results at the full 10 m GSD. Numéro de notice : A2018-540 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.09.018 date de publication en ligne : 21/10/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.09.018 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91554 [article]

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Pan-sharpening via deep metric learning / Yinghui Xing in ISPRS Journal of photogrammetry and remote sensing, vol 145 - part A (November 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 145 - part A (November 2018) . - pp 165 - 183 Titre : Pan-sharpening via deep metric learning Type de document : Article/Communication Auteurs : Yinghui Xing, Auteur ; Min Wang, Auteur ; Shuyuan Yang, Auteur ; Licheng Jiao, Auteur Année de publication : 2018 Article en page(s) : pp 165 - 183 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] classification par réseau neuronal [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] image panchromatique [Termes descripteurs IGN] image Quickbird [Termes descripteurs IGN] image Worldview [Termes descripteurs IGN] pansharpening (fusion d'images) [Termes descripteurs IGN] réseau neuronal convolutif Résumé : (Auteur) Neighbors Embedding based pansharpening methods have received increasing interests in recent years. However, image patches do not strictly follow the similar structure in the shallow MultiSpectral (MS) and PANchromatic (PAN) image spaces, consequently leading to a bias to the pansharpening. In this paper, a new deep metric learning method is proposed to learn a refined geometric multi-manifold neighbor embedding, by exploring the hierarchical features of patches via multiple nonlinear deep neural networks. First of all, down-sampled PAN images from different satellites are divided into a large number of training image patches and are then grouped coarsely according to their shallow geometric structures. Afterwards, several Stacked Sparse AutoEncoders (SSAE) with similar structures are separately constructed and trained by these grouped patches. In the fusion, image patches of the source PAN image pass through the networks to extract features for formulating a deep distance metric and thus deriving their geometric labels. Then, patches with the same geometric labels are grouped to form geometric manifolds. Finally, the assumption that MS patches and PAN patches form the same geometric manifolds in two distinct spaces, is cast on geometric groups to formulate geometric multi-manifold embedding for estimating high resolution MS image patches. Some experiments are taken on datasets acquired by different satellites. The experimental results demonstrate that our proposed method can obtain better fusion results than its counterparts in terms of visual results and quantitative evaluations. Numéro de notice : A2018-493 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.01.016 date de publication en ligne : 17/02/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.01.016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91236 [article]

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Sensitivity analysis of pansharpening in hyperspectral change detection / Seyd Teymoor Seydi in Applied geomatics, vol 10 n° 1 (March 2018)  

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Intersensor statistical matching for pansharpening : theoretical issues and practical solutions / Luciano Alparone in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)  

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Describing contrast across scales / Sohaib Ali Syed in ISPRS Journal of photogrammetry and remote sensing, vol 128 (June 2017)  

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Pan-sharpening of Landsat-8 images and its application in calculating vegetation greenness and canopy water contents / Khan Rubayet Rahaman in ISPRS International journal of geo-information, vol 6 n° 6 (June 2017)  

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Evaluation of pan-sharpening methods for spatial and spectral quality / Jagalingam Pushparaj in Applied geomatics, vol 9 n° 1 (March 2017)  

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Pan-sharpening quality investigation of PLÉIADES-1A images / Mustafa Ozendi in Geocarto international, vol 31 n° 7 - 8 (July - August 2016)  

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Quantitative quality evaluation of pansharpened imagery: consistency versus synthesis / Frosti Palsson in IEEE Transactions on geoscience and remote sensing, vol 54 n° 3 (March 2016)  

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Operationalizing measurement of forest degradation: Identification and quantification of charcoal production in tropical dry forests using very high resolution satellite imagery / K. Dons in International journal of applied Earth observation and geoinformation, vol 39 (July 2015)  

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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)  

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A critical comparison among pansharpening algorithms / Gemine Vivone in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)

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