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Integrating spatio-temporal-spectral information for downscaling Sentinel-3 OLCI images / Yijie Tang in ISPRS Journal of photogrammetry and remote sensing, vol 180 (October 2021)
[article]
Titre : Integrating spatio-temporal-spectral information for downscaling Sentinel-3 OLCI images Type de document : Article/Communication Auteurs : Yijie Tang, Auteur ; Qunming Wang, Auteur ; Xiaohua Tong, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 130 - 150 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] données spatiotemporelles
[Termes IGN] fusion d'images
[Termes IGN] image multibande
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-OLCI
[Termes IGN] pouvoir de résolution géométrique
[Termes IGN] réduction d'échelle
[Termes IGN] réflectanceRésumé : (auteur) Sentinel-3 is a newly launched satellite implemented by the European Space Agency (ESA) for global observation. The Ocean and Land Colour Imager (OLCI) sensor onboard Sentinel-3 provides 21 band images with a fine spectral resolution and is of great value for ocean, land and atmospheric monitoring. The two platforms (Sentinel-3A and -3B) can provide OLCI images at an almost daily temporal resolution. The coarse spatial resolution of the 21 band OLCI images (i.e., 300 m), however, limits greatly their utility for local, precise monitoring. Sentinel-2, another satellite provided by ESA, carries the Multispectral Imager (MSI) sensor which can supply much finer spatial resolution (e.g., 10 m and 20 m) images. This paper introduces a new fusion framework integrating spatio-temporal-spectral information for downscaling Sentinel-3 OLCI images, which has two parts. Based on bands with similar wavelengths (i.e., bands 2, 3, 4 and 8a for Sentinel-2 and bands Oa4, Oa6, Oa8 and Oa17 for Sentinel-3), the four Sentinel-3 bands are first downscaled to the spatial resolution of Sentinel-2 images by applying spatio-temporal fusion to Sentinel-2 MSI and Sentinel-3 OLCI images. Then, to take full advantage of all 21 available OLCI bands of the Sentinel-3 images, the extended image pair-based spatio-spectral fusion (EIPSSF) method is proposed in this paper to downscale the other 17 bands. EIPSSF is performed based on the new concept of the extended image pair (EIP) and by exploiting existing spatio-temporal fusion approaches. The framework consisting of spatio-temporal and spatio-spectral fusion is entirely general, which provides a practical solution for comprehensive downscaling of Sentinel-3 OLCI images for fine spatial, temporal and spectral resolution monitoring. Numéro de notice : A2021-654 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.08.012 Date de publication en ligne : 24/08/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.08.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98384
in ISPRS Journal of photogrammetry and remote sensing > vol 180 (October 2021) . - pp 130 - 150[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2021101 SL Revue Centre de documentation Revues en salle Disponible 081-2021103 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2021102 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Spectral reflectance estimation of UAS multispectral imagery using satellite cross-calibration method / Saket Gowravaram in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 10 (October 2021)
[article]
Titre : Spectral reflectance estimation of UAS multispectral imagery using satellite cross-calibration method Type de document : Article/Communication Auteurs : Saket Gowravaram, Auteur ; Haiyang Chao, Auteur ; Andrew Molthan, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 735 - 746 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] aéronef
[Termes IGN] distribution du coefficient de réflexion bidirectionnelle BRDF
[Termes IGN] étalonnage croisé
[Termes IGN] forêt
[Termes IGN] image captée par drone
[Termes IGN] image Landsat-8
[Termes IGN] image multibande
[Termes IGN] image proche infrarouge
[Termes IGN] Kansas (Etats-Unis ; état)
[Termes IGN] orthoimage
[Termes IGN] orthorectification
[Termes IGN] prairie
[Termes IGN] rayonnement proche infrarouge
[Termes IGN] réflectance spectraleRésumé : (Auteur) This paper introduces a satellite-based cross-calibration (SCC) method for spectral reflectance estimation of unmanned aircraft system (UAS) multispectral imagery. The SCC method provides a low-cost and feasible solution to convert high-resolution UAS images in digital numbers (DN) to reflectance when satellite data is available. The proposed method is evaluated using a multispectral data set, including orthorectified KHawk UAS DN imagery and Landsat 8 Operational Land Imager Level-2 surface reflectance (SR) data over a forest/grassland area. The estimated UAS reflectance images are compared with the National Ecological Observatory Network's imaging spectrometer (NIS) SR data for validation. The UAS reflectance showed high similarities with the NIS data for the near-infrared and red bands with Pearson's r values being 97 and 95.74, and root-mean-square errors being 0.0239 and 0.0096 over a 32-subplot hayfield. Numéro de notice : A2021-676 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.20-00091R2 En ligne : https://doi.org/10.14358/PERS.20-00091R2 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98863
in Photogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 10 (October 2021) . - pp 735 - 746[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2021101 SL Revue Centre de documentation Revues en salle Disponible Classification of tree species in a heterogeneous urban environment using object-based ensemble analysis and World View-2 satellite imagery / Simbarashe Jombo in Applied geomatics, vol 13 n° 3 (September 2021)
[article]
Titre : Classification of tree species in a heterogeneous urban environment using object-based ensemble analysis and World View-2 satellite imagery Type de document : Article/Communication Auteurs : Simbarashe Jombo, Auteur ; Elhadi Adam, Auteur ; John Odindi, Auteur Année de publication : 2021 Article en page(s) : pp 373 - 387 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] arbre urbain
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] espèce végétale
[Termes IGN] image à très haute résolution
[Termes IGN] image multibande
[Termes IGN] image Worldview
[Termes IGN] indice de végétation
[Termes IGN] Johannesbourg
[Termes IGN] segmentation d'imageRésumé : (auteur) Urban trees are valuable in, inter alia, ameliorating air pollution and mitigating the effects associated with urban heat islands. The dearth of tree cover maps is a major challenge for urban planners in the management of urban trees. This work adopts remote sensing approaches to provide urban tree cover maps which can strengthen urban landscape management. Whereas traditional pixel-based classification approaches have been commonly used in image classification, they are not well-suited for urban tree mapping due to their failure to fully explore the image’s spatial and spectral characteristics. Object-based classification techniques produce improved accuracies using additional variables. This study depicts the capability of object-based image analysis (OBIA) in mapping common urban trees using very high-resolution (VHR) WorldView-2 (WV-2) imagery. The study tests the utility of WV-2 bands and other feature variables in the object-based mapping of common urban trees and other land cover classes. Furthermore, the study compares the utility of Support Vector Machine (SVM) and Random Forest (RF) in the object-based mapping of common urban trees and other land cover classes. The results show that the Normalized Difference Vegetation Index (NDVI), NIR 1 and NIR 2 bands were important in the classification of common urban trees and other land cover classes. The RF classifier performed better than SVM, with an overall accuracy of 91.9% as compared to 87.3% for SVM. The results of this study offer insight to urban authorities with knowledge on the segmentation parameters, classification methods and feature variables for mapping urban trees, valuable in urban tree management. Numéro de notice : A2021-624 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1007/s12518-021-00358-3 Date de publication en ligne : 20/01/2021 En ligne : https://doi.org/10.1007/s12518-021-00358-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98248
in Applied geomatics > vol 13 n° 3 (September 2021) . - pp 373 - 387[article]Detection of aspen in conifer-dominated boreal forests with seasonal multispectral drone image point clouds / Alwin A. Hardenbol in Silva fennica, vol 55 n° 4 (September 2021)
[article]
Titre : Detection of aspen in conifer-dominated boreal forests with seasonal multispectral drone image point clouds Type de document : Article/Communication Auteurs : Alwin A. Hardenbol, Auteur ; Anton Kuzmin, Auteur ; Lauri Korhonen, Auteur ; Pasi Korpelainen, Auteur ; Timo Kumpula, Auteur ; Matti Maltamo, Auteur ; Jari Kouki, Auteur Année de publication : 2021 Article en page(s) : n° 10515 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] aire protégée
[Termes IGN] analyse discriminante
[Termes IGN] Betula (genre)
[Termes IGN] détection d'arbres
[Termes IGN] forêt boréale
[Termes IGN] image captée par drone
[Termes IGN] image multibande
[Termes IGN] orthoimage couleur
[Termes IGN] peuplement mélangé
[Termes IGN] Picea abies
[Termes IGN] Pinus sylvestris
[Termes IGN] Populus tremula
[Termes IGN] semis de points
[Termes IGN] variation saisonnièreRésumé : (auteur) Current remote sensing methods can provide detailed tree species classification in boreal forests. However, classification studies have so far focused on the dominant tree species, with few studies on less frequent but ecologically important species. We aimed to separate European aspen (Populus tremula L.), a biodiversity-supporting tree species, from the more common species in European boreal forests (Pinus sylvestris L., Picea abies [L.] Karst., Betula spp.). Using multispectral drone images collected on five dates throughout one thermal growing season (May–September), we tested the optimal season for the acquisition of mono-temporal data. These images were collected from a mature, unmanaged forest. After conversion into photogrammetric point clouds, we segmented crowns manually and automatically and classified the species by linear discriminant analysis. The highest overall classification accuracy (95%) for the four species as well as the highest classification accuracy for aspen specifically (user’s accuracy of 97% and a producer’s accuracy of 96%) were obtained at the beginning of the thermal growing season (13 May) by manual segmentation. On 13 May, aspen had no leaves yet, unlike birches. In contrast, the lowest classification accuracy was achieved on 27 September during the autumn senescence period. This is potentially caused by high intraspecific variation in aspen autumn coloration but may also be related to our date of acquisition. Our findings indicate that multispectral drone images collected in spring can be used to locate and classify less frequent tree species highly accurately. The temporal variation in leaf and canopy appearance can alter the detection accuracy considerably. Numéro de notice : A2021-735 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14214/sf.10515 Date de publication en ligne : 14/07/2021 En ligne : https://doi.org/10.14214/sf.10515 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98691
in Silva fennica > vol 55 n° 4 (September 2021) . - n° 10515[article]Hyperspectral image fusion and multitemporal image fusion by joint sparsity / Han Pan in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 9 (September 2021)
[article]
Titre : Hyperspectral image fusion and multitemporal image fusion by joint sparsity Type de document : Article/Communication Auteurs : Han Pan, Auteur ; Zhongliang Jing, Auteur ; Henry Leung, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 7887 - 7900 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] correction d'image
[Termes IGN] flou
[Termes IGN] fusion d'images
[Termes IGN] image hyperspectrale
[Termes IGN] image multitemporelle
[Termes IGN] image panchromatique
[Termes IGN] pansharpening (fusion d'images)
[Termes IGN] représentation parcimonieuseRésumé : (auteur) Different image fusion systems have been developed to deal with the massive amounts of image data for different applications, such as remote sensing, computer vision, and environment monitoring. However, the generalizability and versatility of these fusion systems remain unknown. This article proposes an efficient regularization framework to achieve different kinds of fusion tasks accounting for the spatiospectral and spatiotemporal variabilities of the fusion process. A joint minimization functional is developed by taking an advantage of a composite regularizer for enforcing joint sparsity in the gradient domain and the frame domain. The proposed composite regularizer is composed of the Hessian Schatten-norm regularization and contourlet-based regularization terms. The resulting problems are solved by the alternating direction method of multipliers (ADMM). The effectiveness of the proposed method is validated in a variety of image fusion experiments: 1) hyperspectral (HS) and panchromatic image fusion; 2) HS and multispectral image fusion; 3) multitemporal image fusion (MIF); and 4) multi-image deblurring. Results show promising performance compared with state-of-the-art fusion methods. Numéro de notice : A2021-649 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3039046 Date de publication en ligne : 07/12/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3039046 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98360
in IEEE Transactions on geoscience and remote sensing > Vol 59 n° 9 (September 2021) . - pp 7887 - 7900[article]Large-area inventory of species composition using airborne laser scanning and hyperspectral data / Hans Ole Ørka in Silva fennica, vol 55 n° 4 (September 2021)PermalinkMulti-task fully convolutional network for tree species mapping in dense forests using small training hyperspectral data / Laura Elena Cué La Rosa in ISPRS Journal of photogrammetry and remote sensing, vol 179 (September 2021)PermalinkTwo hidden layer neural network-based rotation forest ensemble for hyperspectral image classification / Laxmi Narayana Eeti in Geocarto international, vol 36 n° 16 ([01/09/2021])PermalinkUnsupervised band selection of hyperspectral data based on mutual information derived from weighted cluster entropy for snow classification / Divyesh Varade in Geocarto international, vol 36 n° 15 ([15/08/2021])PermalinkDetail injection-based deep convolutional neural networks for pansharpening / Liang-Jian Deng in IEEE Transactions on geoscience and remote sensing, vol 59 n° 8 (August 2021)PermalinkTarget-constrained interference-minimized band selection for hyperspectral target detection / Xiaodi Shang in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 7 (July 2021)PermalinkForest cover mapping and Pinus species classification using very high-resolution satellite images and random forest / Laura Alonso-Martinez in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2021 (July 2021)PermalinkAn incremental isomap method for hyperspectral dimensionality reduction and classification / Yi Ma in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 6 (June 2021)PermalinkApplication of feature selection methods and machine learning algorithms for saltmarsh biomass estimation using Worldview-2 imagery / Sikdar M. M. Rasel in Geocarto international, vol 36 n° 10 ([01/06/2021])PermalinkEvaluating the performance of hyperspectral leaf reflectance to detect water stress and estimation of photosynthetic capacities / Jingjing Zhou in Remote sensing, vol 13 n° 11 (June-1 2021)Permalink