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Fusion of Landsat 8 OLI and sentinel-2 MSI data / Qunming Wang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 7 (July 2017)
[article]
Titre : Fusion of Landsat 8 OLI and sentinel-2 MSI data Type de document : Article/Communication Auteurs : Qunming Wang, Auteur ; George Alan Blackburn, Auteur ; Alex O. Onojeghuo, Auteur Année de publication : 2017 Article en page(s) : pp 3885 - 3899 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] détection de changement
[Termes IGN] fusion d'images
[Termes IGN] image à haute résolution
[Termes IGN] image Landsat-8
[Termes IGN] image Landsat-OLI
[Termes IGN] occupation du sol
[Termes IGN] pouvoir de résolution géométrique
[Termes IGN] surveillanceRésumé : (Auteur) Sentinel-2 is a wide-swath and fine spatial resolution satellite imaging mission designed for data continuity and enhancement of the Landsat and other missions. The Sentinel-2 data are freely available at the global scale, and have similar wavelengths and the same geographic coordinate system as the Landsat data, which provides an excellent opportunity to fuse these two types of satellite sensor data together. In this paper, a new approach is presented for the fusion of Landsat 8 Operational Land Imager and Sentinel-2 Multispectral Imager data to coordinate their spatial resolutions for continuous global monitoring. The 30 m spatial resolution Landsat 8 bands are downscaled to 10 m using available 10 m Sentinel-2 bands. To account for the land-cover/land-use (LCLU) changes that may have occurred between the Landsat 8 and Sentinel-2 images, the Landsat 8 panchromatic (PAN) band was also incorporated in the fusion process. The experimental results showed that the proposed approach is effective for fusing Landsat 8 with Sentinel-2 data, and the use of the PAN band can decrease the errors introduced by LCLU changes. By fusion of Landsat 8 and Sentinel-2 data, more frequent observations can be produced for continuous monitoring (this is particularly valuable for areas that can be covered easily by clouds, thereby, contaminating some Landsat or Sentinel-2 observations), and the observations are at a consistent fine spatial resolution of 10 m. The products have great potential for timely monitoring of rapid changes. Numéro de notice : A2017-489 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2683444 En ligne : http://dx.doi.org/10.1109/TGRS.2017.2683444 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86416
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 7 (July 2017) . - pp 3885 - 3899[article]Fusion of RADARSAT-2 and multispectral optical remote sensing data for LULC extraction in a tropical agricultural area / Mohamed Barakat A. Gibril in Geocarto international, vol 32 n° 7 (July 2017)
[article]
Titre : Fusion of RADARSAT-2 and multispectral optical remote sensing data for LULC extraction in a tropical agricultural area Type de document : Article/Communication Auteurs : Mohamed Barakat A. Gibril, Auteur ; Suzana Bakar, Auteur ; Kouame Yao, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 735 - 748 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] classification pixellaire
[Termes IGN] fusion d'images
[Termes IGN] image Landsat-8
[Termes IGN] image optique
[Termes IGN] image radar
[Termes IGN] image Radarsat
[Termes IGN] Malaisie
[Termes IGN] occupation du sol
[Termes IGN] précision de la classification
[Termes IGN] surface cultivée
[Termes IGN] utilisation du sol
[Termes IGN] zone intertropicaleRésumé : (Auteur) In this study, we investigated the performance of different fusion and classification techniques for land cover mapping in Hilir Perak, Peninsula Malaysia using RADAR and Landsat-8 images in a predominantly agricultural area. The fusion methods used are Brovey Transform, Wavelet Transform, Ehlers and Layer Stacking and their results classified into seven different land cover classes which include (1) pixel-based classifiers (spectral angle mapper (SAM), maximum likelihood (ML), support vector machine (SVM)) and (2) Object-based (rule-based and standard nearest neighbour (NN)) classifiers. The result shows that pixel-based classification achieved maximum accuracy of the optical data classification using SVM in Landsat-8 with 74.96% accuracy compared to SAM and ML. For multisource data classification, the highest overall accuracy recorded for layer stacking (SVM) was 79.78%, Ehlers fusion (SVM) with 45.57%, Brovey fusion (SVM) with 63.70% and Wavelet fusion (SVM) 61.16%. And for object-based classifiers, the overall classification accuracy is 95.35% for rule-based and 76.33% for NN classifier, respectively. Based on the analysis of their performances, object-based and the rule-based classifiers produced the best classification accuracy from the fused images. Numéro de notice : A2017-453 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2016.1170893 Date de publication en ligne : 15/04/2016 En ligne : http://dx.doi.org/10.1080/10106049.2016.1170893 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86373
in Geocarto international > vol 32 n° 7 (July 2017) . - pp 735 - 748[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2017071 RAB Revue Centre de documentation En réserve L003 Disponible A novel automatic method for the fusion of ALS and TLS LiDAR data for robust assessment of tree crown structure / Claudia Paris in IEEE Transactions on geoscience and remote sensing, vol 55 n° 7 (July 2017)
[article]
Titre : A novel automatic method for the fusion of ALS and TLS LiDAR data for robust assessment of tree crown structure Type de document : Article/Communication Auteurs : Claudia Paris, Auteur ; David Kelbe, Auteur ; Jan Van Aardt, Auteur ; Lorenzo Bruzzone, Auteur Année de publication : 2017 Article en page(s) : pp 3679 - 3693 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] canopée
[Termes IGN] corrélation croisée normalisée
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] fusion d'images
[Termes IGN] semis de points
[Termes IGN] télémétrie laser aéroporté
[Termes IGN] télémétrie laser sur satelliteRésumé : (Auteur) Tree crown structural parameters are key inputs to studies spanning forest fire propagation, invasive species dynamics, avian habitat provision, and so on, but these parameters consistently are difficult to measure. While airborne laser scanning (ALS) provides uniform data and a consistent nadir perspective necessary for crown segmentation, the data characteristics of terrestrial laser scanning (TLS) make such crown segmentation efforts much more challenging. We present a data fusion approach to extract crown structure from TLS, by exploiting the complementary perspective of ALS. Multiple TLS point clouds are automatically registered to a single ALS point cloud by maximizing the normalized cross correlation between the global ALS canopy height model (CHM) and each of the local TLS CHMs through parameter optimization of a planar Euclidean transform. Per-tree canopy segmentation boundaries, which are reliably obtained from ALS, can then be adapted onto the more irregular TLS data. This is repeated for each TLS scan; the combined segmentation results from each registered TLS scan and the ALS data are fused into a single per-tree point cloud, from which canopy-level structural parameters readily can be extracted. Numéro de notice : A2017-485 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2675963 En ligne : http://dx.doi.org/10.1109/TGRS.2017.2675963 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86407
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 7 (July 2017) . - pp 3679 - 3693[article]Describing contrast across scales / Sohaib Ali Syed in ISPRS Journal of photogrammetry and remote sensing, vol 128 (June 2017)
[article]
Titre : Describing contrast across scales Type de document : Article/Communication Auteurs : Sohaib Ali Syed, Auteur ; Muhammad Zafar Iqbal, Auteur ; Muhammad Mohsin Riaz, Auteur Année de publication : 2017 Article en page(s) : pp 326 - 337 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] accentuation d'image
[Termes IGN] affinage d'image
[Termes IGN] amélioration du contraste
[Termes IGN] analyse multirésolution
[Termes IGN] contraste local
[Termes IGN] image numérique
[Termes IGN] pansharpening (fusion d'images)Résumé : (Auteur) Due to its sensitive nature against illumination and noise distributions, contrast is not widely used for image description. On the contrary, the human perception of contrast along different spatial frequency bandwidths provides a powerful discriminator function that can be modeled in a robust manner against local illumination. Based upon this observation, a dense local contrast descriptor is proposed and its potential in different applications of computer vision is discussed. Extensive experiments reveal that this simple yet effective description performs well in comparison with state of the art image descriptors. We also show the importance of this description in multiresolution pansharpening framework. Numéro de notice : A2017-337 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.04.002 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.04.002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85504
in ISPRS Journal of photogrammetry and remote sensing > vol 128 (June 2017) . - pp 326 - 337[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2017061 RAB Revue Centre de documentation En réserve L003 Disponible 081-2017063 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2017062 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt Integration of SSC TerraSAR-X images into multisource rapid mapping / D. Vassilaki in Photogrammetric record, vol 32 n° 158 (June - july 2017)
[article]
Titre : Integration of SSC TerraSAR-X images into multisource rapid mapping Type de document : Article/Communication Auteurs : D. Vassilaki, Auteur ; Athanassios A. Stamos, Auteur ; Charalabos Ioannnidis, Auteur Année de publication : 2017 Article en page(s) : pp 160 - 181 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] correction géométrique
[Termes IGN] fusion d'images
[Termes IGN] gestion de crise
[Termes IGN] image optique
[Termes IGN] image radar moirée
[Termes IGN] image TerraSAR-X
[Termes IGN] intégration de données
[Termes IGN] modèle numérique de surfaceRésumé : (Auteur) This paper presents a global automatic process for the integration of satellite synthetic aperture radar (SAR) images with rapid mapping for crisis management. The process consists of methods for the rapid geometric correction of slant-range TerraSAR-X images, and the geometric co-registration and radiometric merging of SAR data with satellite optical images using global digital elevation models (DEMs) and geoid models. The process is invariant to both radiometry and geometry, and is applied to high-resolution Single-look Slant-range Complex (SSC) TerraSAR-X images over suburban and rural areas. Numéro de notice : A2017-363 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12192 En ligne : http://dx.doi.org/10.1111/phor.12192 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85774
in Photogrammetric record > vol 32 n° 158 (June - july 2017) . - pp 160 - 181[article]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)PermalinkHyperspectral and lidar intensity data fusion : A framework for the rigorous correction of illumination, anisotropic effects, and cross calibration / Maximilian Brell in IEEE Transactions on geoscience and remote sensing, vol 55 n° 5 (May 2017)PermalinkEvaluation of pan-sharpening methods for spatial and spectral quality / Jagalingam Pushparaj in Applied geomatics, vol 9 n° 1 (March 2017)PermalinkSemantic segmentation of forest stands of pure species combining airborne lidar data and very high resolution multispectral imagery / Clément Dechesne in ISPRS Journal of photogrammetry and remote sensing, vol 126 (April 2017)PermalinkToward optimum fusion of thermal hyperspectral and visible images in classification of urban area / Farhad Samadzadegan in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 4 (April 2017)PermalinkFusing meter-resolution 4-D InSAR point clouds and optical images for semantic urban infrastructure monitoring / Yuanyuan Wang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 1 (January 2017)PermalinkHigh-quality seamless DEM generation blending SRTM-1, ASTER GDEM v2 and ICESat/GLAS observations / Linwei Yue in ISPRS Journal of photogrammetry and remote sensing, vol 123 (January 2017)PermalinkA two-step decision fusion strategy: application to hyperspectral and multispectral images for urban classification / Walid Ouerghemmi (2017)PermalinkUtilisation d’image THR et drone pour l’étude de la dynamique côtière d’Ouvéa (Île des Loyautés - Nouvelle Calédonie) / Sabrina Bosque (2017)PermalinkAn integrated framework for the spatio–temporal–spectral fusion of remote sensing images / Huanfeng Shen in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)Permalink