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Sentinel-2 sharpening using a reduced-rank method / Magnus Orn Ulfarsson in IEEE Transactions on geoscience and remote sensing, vol 57 n° 9 (September 2019)
[article]
Titre : Sentinel-2 sharpening using a reduced-rank method Type de document : Article/Communication Auteurs : Magnus Orn Ulfarsson, Auteur ; Frosti Palsson, Auteur ; Mauro Dalla Mura, Auteur ; Johannes R. Sveinsson, Auteur Année de publication : 2019 Article en page(s) : pp 6408 - 6420 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] affinage d'image
[Termes IGN] ajustement de paramètres
[Termes IGN] estimation bayesienne
[Termes IGN] fusion de données
[Termes IGN] image à haute résolution
[Termes IGN] image multibande
[Termes IGN] image Sentinel-MSI
[Termes IGN] largeur de bandeRésumé : (auteur) Recently, the Sentinel-2 (S2) satellite constellation was deployed for mapping and monitoring the Earth environment. Images acquired by the sensors mounted on the S2 platforms have three levels of spatial resolution: 10, 20, and 60 m. In many remote sensing applications, the availability of images at the highest spatial resolution (i.e., 10 m for S2) is often desirable. This can be achieved by generating a synthetic high-resolution image through data fusion. To this end, researchers have proposed techniques exploiting the spectral/spatial correlation inherent in multispectral data to sharpen the lower resolution S2 bands to 10 m. In this paper, we propose a novel method that formulates the sharpening process as a solution to an inverse problem. We develop a cyclic descent algorithm called S2Sharp and an associated tuning parameter selection algorithm based on generalized cross validation and Bayesian optimization. The tuning parameter selection method is evaluated on a simulated data set. The effectiveness of S2Sharp is assessed experimentally by comparisons to state-of-the-art methods using both simulated and real data sets. Numéro de notice : A2019-340 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2906048 Date de publication en ligne : 22/04/2019 En ligne : http://doi.org/10.1109/TGRS.2019.2906048 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93377
in IEEE Transactions on geoscience and remote sensing > vol 57 n° 9 (September 2019) . - pp 6408 - 6420[article]A generalized space-time OBIA classification scheme to map sugarcane areas at regional scale, using Landsat images time-series and the random forest algorithm / Ana Claudia Dos Santos Luciano in International journal of applied Earth observation and geoinformation, vol 80 (August 2019)
[article]
Titre : A generalized space-time OBIA classification scheme to map sugarcane areas at regional scale, using Landsat images time-series and the random forest algorithm Type de document : Article/Communication Auteurs : Ana Claudia Dos Santos Luciano, Auteur ; Michelle Cristina Araújo Picoli, Auteur ; Jansle Vieira Rocha, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 127-136 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse spatio-temporelle
[Termes IGN] apprentissage automatique
[Termes IGN] Brésil
[Termes IGN] carte agricole
[Termes IGN] classification dirigée
[Termes IGN] classification orientée objet
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] extraction de données
[Termes IGN] image à haute résolution
[Termes IGN] image Landsat
[Termes IGN] production agricole
[Termes IGN] Saccharum officinarum
[Termes IGN] série temporelle
[Termes IGN] surface cultivée
[Termes IGN] zone d'intérêtRésumé : (auteur) The monitoring of sugarcane areas is important for sustainable planning and management of the sugarcane industry in Brazil. We developed an operational Object-Based Image Analysis (OBIA) classification scheme, with generalized space-time classifier, for mapping sugarcane areas at the regional scale in São Paulo State (SP). Binary random forest (RF) classification models were calibrated using multi-temporal data from Landsat images, at 10 sites located across SP. Space and time generalization were tested and compared for three approaches: a local calibration and application; a cross-site spatial generalization test with the RF model calibrated on a site and applied on other sites; and a unique space–time classifier calibrated with all sites together on years 2009–2014 and applied to the entire SP region on 2015. The local RF models Dice Coefficient (DC) accuracies at sites 1 to 8 were between 0.83 and 0.92 with an average of 0.89. The cross-site classification accuracy showed an average DC of 0.85, and the unique RF model had a DC of 0.89 when compared with a reference map of 2015. The results demonstrated a good relationship between sugarcane prediction and the reference map for each municipality in SP, with R² = 0.99 and only 5.8% error for the total sugarcane area in SP, and compared with the area inventory from the Brazilian Institute of Geography and Statistics, with R² = 0.95 and –1% error for the total sugarcane area in SP. The final unique RF model allowed monitoring sugarcane plantations at the regional scale on independent year, with efficiency, low-cost, limited resources and a precision approximating that of a photointerpretation. Numéro de notice : A2019-470 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2019.04.013 Date de publication en ligne : 25/04/2019 En ligne : https://doi.org/10.1016/j.jag.2019.04.013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93612
in International journal of applied Earth observation and geoinformation > vol 80 (August 2019) . - pp 127-136[article]High-resolution large-area digital orthophoto map generation using LROC NAC images / Kaichang Di in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 7 (July 2019)
[article]
Titre : High-resolution large-area digital orthophoto map generation using LROC NAC images Type de document : Article/Communication Auteurs : Kaichang Di, Auteur ; Jia Mengna, Auteur ; Xin Xin, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 481 - 491 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Orthophotographie, orthoimage
[Termes IGN] Chine
[Termes IGN] compensation par bloc
[Termes IGN] erreur de positionnement
[Termes IGN] image à haute résolution
[Termes IGN] Lune
[Termes IGN] modèle géométrique de prise de vue
[Termes IGN] modèle numérique de terrain
[Termes IGN] orthoimage
[Termes IGN] orthophotoplan numérique
[Termes IGN] zone homogèneRésumé : (auteur) The Chang'e-5 mission of China is planned to be launched in 2019 to the landing area near Mons Rümker located in Oceanus Procellarum. Aiming to generate a high-resolution and high-quality digital orthophoto map (DOM) of the planned landing area for supporting the mission and various scientific analyses, this study developed a systematic and effective method for large-area seamless DOM production. The mapping results of the Chang'e-5 landing area using over 700 Lunar Reconnaissance Orbiter Camera (LROC) Narrow Angle Camera (NAC) images are presented. The resultant seamless DOM has a resolution of 1.5 m, covers a large area of 20° in longitude and 4° in latitude, and is tied to SLDEM2015. The results demonstrate that the proposed method can reduce the geometric inconsistencies among the LROC NAC images to the subpixel level and the positional errors with respect to the reference digital elevation model to about one grid cell size. Numéro de notice : A2019-257 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.7.481 Date de publication en ligne : 01/07/2019 En ligne : https://doi.org/10.14358/PERS.85.7.481 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93052
in Photogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 7 (July 2019) . - pp 481 - 491[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2019071 SL Revue Centre de documentation Revues en salle Disponible A novel method for separating woody and herbaceous time series / Qiang Zhou in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 7 (July 2019)
[article]
Titre : A novel method for separating woody and herbaceous time series Type de document : Article/Communication Auteurs : Qiang Zhou, Auteur ; Shuguang Liu, Auteur ; Michael J Hill, Auteur Année de publication : 2019 Article en page(s) : pp 509 - 520 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Afrique australe
[Termes IGN] bois
[Termes IGN] extraction de la végétation
[Termes IGN] image à haute résolution
[Termes IGN] image Ikonos
[Termes IGN] image Landsat-SWIR
[Termes IGN] image Terra-MODIS
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] plante herbacée
[Termes IGN] savane
[Termes IGN] série temporelle
[Termes IGN] variation saisonnièreRésumé : (auteur) Mapping the spatial distribution of woody and herbaceous vegetation in high temporal resolution in savannas would be beneficial for modeling interrelationships between trees and grasses, and monitoring fuel loads and biomass for livestock. In this study, we developed a frequency decomposition method to separate woody and herbaceous vegetation components using Normalized Difference Vegetation Index (NDVI) time series. The results were validated using fractional cover data derived from high-resolution images. The validation revealed a close relationship between our decomposed NDVI and corresponding fractional cover (R2 = 0.55 and 0.64 for woody and herbaceous components, respectively). We examined the spatial and temporal patterns of the decomposed NDVI, where woody and herbaceous NDVI showed different responses to precipitation. The methods proposed in this study can be used to separate the woody and herbaceous NDVI time series as an alternative approach for monitoring woody and herbaceous vegetation interrelationships related to climatic drivers. Numéro de notice : A2019-259 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.7.509 Date de publication en ligne : 01/07/2019 En ligne : https://doi.org/10.14358/PERS.85.7.509 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93062
in Photogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 7 (July 2019) . - pp 509 - 520[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2019071 SL Revue Centre de documentation Revues en salle Disponible A cognitive framework for road detection from high-resolution satellite images / Naveen Chandra in Geocarto international, vol 34 n° 8 ([15/06/2019])
[article]
Titre : A cognitive framework for road detection from high-resolution satellite images Type de document : Article/Communication Auteurs : Naveen Chandra, Auteur ; Jayanta Kumar Ghosh, Auteur ; Ashu Sharma, Auteur Année de publication : 2019 Article en page(s) : pp 909 - 924 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] cadre conceptuel
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] extraction du réseau routier
[Termes IGN] image à haute résolution
[Termes IGN] image satellite
[Termes IGN] méthode fondée sur le noyau
[Termes IGN] représentation cognitive
[Termes IGN] zone urbaineRésumé : (auteur) Road network extraction from high-resolution satellite (HRS) imagery is a complex task. It is an important field of research and is widely used in various cartographic applications such as updating and generating maps. The objective of this research work is to develop a novel framework, emulating human cognition, for detection of roads from HRS images. Roads network from HRS images are detected using support vector machines within the different stages of cognitive task analysis. In the first stage, basic information about the cognitive parameters which are required for image interpretation is collected. In the second stage, the rule-based method is used for knowledge representation. Lastly, during knowledge elicitation, the developed rules are used to extract roads from HRS images. The proposed method is validated using 16 HRS images of developed suburban, developed urban, emerging suburban and emerging urban region. Numéro de notice : A2019-515 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1450451 Date de publication en ligne : 29/03/2018 En ligne : https://doi.org/10.1080/10106049.2018.1450451 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93869
in Geocarto international > vol 34 n° 8 [15/06/2019] . - pp 909 - 924[article]Automatic building extraction from high-resolution aerial images and LiDAR data using gated residual refinement network / Jianfeng Huang in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)PermalinkExploring semantic elements for urban scene recognition: Deep integration of high-resolution imagery and OpenStreetMap (OSM) / Wenzhi Zhao in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)PermalinkIncluding Sentinel-1 radar data to improve the disaggregation of MODIS land surface temperature data / Abdelhakim Amazirh in ISPRS Journal of photogrammetry and remote sensing, vol 150 (April 2019)PermalinkSegmentation for Object-Based Image Analysis (OBIA): A review of algorithms and challenges from remote sensing perspective / Mohammad D. Hossain in ISPRS Journal of photogrammetry and remote sensing, vol 150 (April 2019)PermalinkComparaison de MNT à haute résolution issus de techniques laser et photogrammétriques / Michel Kasser in XYZ, n° 158 (mars 2019)PermalinkDuPLO: A DUal view Point deep Learning architecture for time series classificatiOn / Roberto Interdonato in ISPRS Journal of photogrammetry and remote sensing, vol 149 (March 2019)PermalinkEvaluation of time-series SAR and optical images for the study of winter land-use / Julien Denize (2019)PermalinkPermalinkIndividual tree detection and crown delineation with 3D information from multi-view satellite Images / Changlin Xiao in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 1 (January 2019)PermalinkA multi-faceted CNN architecture for automatic classification of mobile LiDAR data and an algorithm to reproduce point cloud samples for enhanced training / Bhavesh Kumar in ISPRS Journal of photogrammetry and remote sensing, vol 147 (January 2019)PermalinkPermalinkSensitivity of urban material classification to spatial and spectral configurations from visible to short-wave infrared / Arnaud Le Bris (2019)PermalinkPermalinkAutomatic building rooftop extraction from aerial images via hierarchical RGB-D priors / Shibiao Xu in IEEE Transactions on geoscience and remote sensing, vol 56 n° 12 (December 2018)PermalinkScene classification based on multiscale convolutional neural network / Yanfei Liu in IEEE Transactions on geoscience and remote sensing, vol 56 n° 12 (December 2018)PermalinkPermalinkRemote sensing scene classification by unsupervised representation learning / Xiaoqiang Lu in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)PermalinkA Stepwise-Then-Orthogonal Regression (STOR) with quality control for optimizing the RFM of high-resolution satellite imagery / Chang Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 9 (September 2017)PermalinkSimultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks / Rasha Alshehhi in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)PermalinkExtrapolated georeferencing of high-resolution satellite imagery based on the strip constraint / Jinshan Cao in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 7 (July 2017)PermalinkFusion 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)PermalinkJoint hyperspectral superresolution and unmixing with interactive feedback / Chen Yi in IEEE Transactions on geoscience and remote sensing, vol 55 n° 7 (July 2017)PermalinkSuperresolution for UAV images via adaptive multiple sparse representation and its application to 3-D reconstruction / Muhammad Haris in IEEE Transactions on geoscience and remote sensing, vol 55 n° 7 (July 2017)PermalinkCan a machine generate humanlike language descriptions for a remote sensing image? / Zhenwei Shi in IEEE Transactions on geoscience and remote sensing, vol 55 n° 6 (June 2017)PermalinkHyperspectral band selection from statistical wavelet models / Siwei Feng in IEEE Transactions on geoscience and remote sensing, vol 55 n° 4 (April 2017)PermalinkSpatiotemporal downscaling approaches for monitoring 8-day 30 m actual evapotranspiration / Yinghai Ke in ISPRS Journal of photogrammetry and remote sensing, vol 126 (April 2017)PermalinkCartographie de l'occupation des sols à partir de séries temporelles d'images satellitaires à hautes résolutions : identification et traitement des données mal étiquetées / Charlotte Pelletier (2017)PermalinkRaft cultivation area extraction from high resolution remote sensing imagery by fusing multi-scale region-line primitive association features / Wang Min in ISPRS Journal of photogrammetry and remote sensing, vol 123 (January 2017)PermalinkSingle Image Super-Resolution based on Neural Networks for text and face recognition / Clément Peyrard (2017)PermalinkMultiband image fusion based on spectral unmixing / Qi Wei in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)PermalinkAn operational high-resolution forest inventory / Julianno Sambatti in GIM international [en ligne], vol 30 n° 10 (October 2016)PermalinkCorrection of atmospheric refraction geolocation error for high resolution optical satellite pushbroom images / Ming Yan in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 6 (June 2016)PermalinkA manifold alignment approach for hyperspectral image visualization with natural color / Danping Liao in IEEE Transactions on geoscience and remote sensing, vol 54 n° 6 (June 2016)PermalinkToward a generalizable image representation for large-scale change detection : application to generic damage analysis / Lionel Gueguen in IEEE Transactions on geoscience and remote sensing, vol 54 n° 6 (June 2016)PermalinkInformation from imagery: ISPRS scientific vision and research agenda / Jun Chen in ISPRS Journal of photogrammetry and remote sensing, vol 115 (May 2016)PermalinkA penalized spline-based attitude model for high-resolution satellite imagery / Hongbo Pan in IEEE Transactions on geoscience and remote sensing, vol 54 n° 3 (March 2016)PermalinkDEM-assisted RFM block adjustment of pushbroom nadir viewing HRS imagery / Yongjun Zhang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 2 (February 2016)PermalinkAn assessment of image features and random forest for land cover mapping over large areas using high resolution Satellite Image Time Series / Charlotte Pelletier (2016)PermalinkPermalinkDigital surface model generation over urban areas using high resolution satellite SAR imagery : tomographic techniques and their application to 3-Dchange monitoring / Martina Porfiri (2016)PermalinkAnalysis of different methods for 3D reconstruction of natural surfaces from parallel-axes UAV images / Annette Eltner in Photogrammetric record, vol 30 n° 151 (September - November 2015)PermalinkImages satellite : de nouveaux capteurs, un accès facilité aux données et des produits innovants / H. Heisig in Géomatique suisse, vol 113 n° 9 (septembre 2015)PermalinkToward evaluating multiscale segmentations of high spatial resolution remote sensing images / Xueliang Zhang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 7 (July 2015)PermalinkMangrove tree crown delineation from high-resolution imagery / Muditha K. Heenkenda in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 6 (June 2015)PermalinkA critical comparison among pansharpening algorithms / Gemine Vivone in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)Permalink