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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)
[article]
Titre : Large-area inventory of species composition using airborne laser scanning and hyperspectral data Type de document : Article/Communication Auteurs : Hans Ole Ørka, Auteur ; Endre H. Hansen, Auteur ; Michele Dalponte, Auteur ; Terje Gobakken, Auteur ; Erik Naesset, Auteur Année de publication : 2021 Article en page(s) : n° 10244 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] composition d'un peuplement forestier
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] image hyperspectrale
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] Norvège
[Termes IGN] Picea abies
[Termes IGN] Pinus sylvestris
[Termes IGN] régression
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) Tree species composition is an essential attribute in stand-level forest management inventories and remotely sensed data might be useful for its estimation. Previous studies on this topic have had several operational drawbacks, e.g., performance studied at a small scale and at a single tree-level with large fieldwork costs. The current study presents the results from a large-area inventory providing species composition following an operational area-based approach. The study utilizes a combination of airborne laser scanning and hyperspectral data and 97 field sample plots of 250 m2 collected over 350 km2 of productive forest in Norway. The results show that, with the availability of hyperspectral data, species-specific volume proportions can be provided in operational forest management inventories with acceptable results in 90% of the cases at the plot level. Dominant species were classified with an overall accuracy of 91% and a kappa-value of 0.73. Species-specific volumes were estimated with relative root mean square differences of 34%, 87%, and 102% for Norway spruce (Picea abies (L.) Karst.), Scots pine (Pinus sylvestris L.), and deciduous species, respectively. A novel tree-based approach for selecting pixels improved the results compared to a traditional approach based on the normalized difference vegetation index. Numéro de notice : A2021-736 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14214/sf.10244 En ligne : https://doi.org/10.14214/sf.10244 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98695
in Silva fennica > vol 55 n° 4 (September 2021) . - n° 10244[article]Metaheuristics for the positioning of 3D objects based on image analysis of complementary 2D photographs / Arnaud Flori in Machine Vision and Applications, vol 32 n° 5 (September 2021)
[article]
Titre : Metaheuristics for the positioning of 3D objects based on image analysis of complementary 2D photographs Type de document : Article/Communication Auteurs : Arnaud Flori, Auteur ; Hamouche Oulhadj, Auteur ; Patrick Siarry, Auteur Année de publication : 2021 Article en page(s) : n° 105 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme du recuit simulé
[Termes IGN] analyse d'image orientée objet
[Termes IGN] contour
[Termes IGN] image 2D
[Termes IGN] modélisation 3D
[Termes IGN] optimisation par essaim de particules
[Termes IGN] scène 3D
[Termes IGN] triangulationRésumé : (auteur) Today, advances in 3D modeling make it possible to identically reproduce objects, animals, humans and even entire scenes. The broad applications concern video games, virtual reality or augmented reality and cinema, for example. In this article, we propose a new method to build a 3D scene directly from several complementary photographs. The positions of the objects for which we already have a 3D model will be determined by triangulation, thanks to the information extracted from the photographs, such as the outline of the objects on the images. Each pixel of the images is converted into a value that gives its distance to the nearest outline. The 3D model of the objects is then projected on the converted images, and the triangulation is done using a cost function that gives the distance of each projection of the objects to their respective outlines. A projection is considered perfect when its distance to its outlines is null, which means that the cost function gives a score of zero as well. We propose to solve this optimization problem by means of two algorithms, namely Simulated Annealing (SA) and quantum particle swarm optimization (QUAPSO). Numéro de notice : A2021-868 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00138-021-01229-y Date de publication en ligne : 03/08/2021 En ligne : https://doi.org/10.1007/s00138-021-01229-y Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99101
in Machine Vision and Applications > vol 32 n° 5 (September 2021) . - n° 105[article]Multi-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)
[article]
Titre : Multi-task fully convolutional network for tree species mapping in dense forests using small training hyperspectral data Type de document : Article/Communication Auteurs : Laura Elena Cué La Rosa, Auteur ; Camile Sothe, Auteur ; Raul Queiroz Feitosa, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 35 - 49 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] Brésil
[Termes IGN] carte de la végétation
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] densité de la végétation
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] espèce végétale
[Termes IGN] forêt tropicale
[Termes IGN] image captée par drone
[Termes IGN] image hyperspectrale
[Termes IGN] segmentation sémantiqueRésumé : (Auteur) This work proposes a multi-task fully convolutional architecture for tree species mapping in dense forests from sparse and scarce polygon-level annotations using hyperspectral UAV-borne data. Our model implements a partial loss function that enables dense tree semantic labeling outcomes from non-dense training samples, and a distance regression complementary task that enforces tree crown boundary constraints and substantially improves the model performance. Our multi-task architecture uses a shared backbone network that learns common representations for both tasks and two task-specific decoders, one for the semantic segmentation output and one for the distance map regression. We report that introducing the complementary task boosts the semantic segmentation performance compared to the single-task counterpart in up to 11% reaching an average user’s accuracy of 88.63% and an average producer’s accuracy of 88.59%, achieving state-of-art performance for tree species classification in tropical forests. Numéro de notice : A2021-575 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.07.001 Date de publication en ligne : 28/07/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.07.001 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98175
in ISPRS Journal of photogrammetry and remote sensing > vol 179 (September 2021) . - pp 35 - 49[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2021091 SL Revue Centre de documentation Revues en salle Disponible 081-2021093 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2021092 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Target-based automated matching of multiple terrestrial laser scans for complex forest scenes / Xuming Ge in ISPRS Journal of photogrammetry and remote sensing, vol 179 (September 2021)
[article]
Titre : Target-based automated matching of multiple terrestrial laser scans for complex forest scenes Type de document : Article/Communication Auteurs : Xuming Ge, Auteur ; Qing Zhu, Auteur Année de publication : 2021 Article en page(s) : pp 1 - 13 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] appariement de données localisées
[Termes IGN] biomasse aérienne
[Termes IGN] biomasse forestière
[Termes IGN] densité de la végétation
[Termes IGN] détection d'arbres
[Termes IGN] diamètre à hauteur de poitrine
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] scène forestière
[Termes IGN] semis de pointsRésumé : (Auteur) Terrestrial laser scanners are widely used to derive unbiased and non-destructive estimates of the vertical distribution of the plant area index and plant area volume density at plot-level scales, as well as the above-ground biomass, height, and diameter at breast height of individual trees. Multiple scans are often employed to capture and register data so that all of the stems can be detected and their complete forms can be analyzed. Researchers have traditionally preferred target-less strategies to register scans because of their low cost and convenience. However, in complex forest scenes, even state-of-the-art approaches cannot guarantee the success of any pairwise registration. In this study, we present an automated target-based processing approach for the registration of unordered scans in complex forest scenes. In contrast to previous studies, the proposed registration method automatically detects the artificial targets and builds a geometric network to judge their connectivity. A pose graph is then exploited to combine these data with the corresponding pairwise transformation, and then the scans are integrated into a unified coordinate system. This method is more robust and efficient than target-less approaches because it is independent of the characteristics of individual trees and does not require ground information. In an experimental scenario, we use an extremely complex wild bamboo forest scene to evaluate the performance of the proposed approach in terms of robustness, accuracy, and efficiency. Numéro de notice : A2021-573 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.06.019 Date de publication en ligne : 15/07/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.06.019 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98173
in ISPRS Journal of photogrammetry and remote sensing > vol 179 (September 2021) . - pp 1 - 13[article]Réservation
Réserver ce documentExemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2021091 SL Revue Centre de documentation Revues en salle Disponible 081-2021093 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2021092 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Towards culture-aware smart and sustainable cities: Integrating historical sources in spatial information infrastructures / Bénédicte Bucher in ISPRS International journal of geo-information, vol 10 n° 9 (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])PermalinkSingle annotated pixel based weakly supervised semantic segmentation under driving scenes / Xi Li in Pattern recognition, vol 116 (August 2021)PermalinkStructure-aware indoor scene reconstruction via two levels of abstraction / Hao Fang in ISPRS Journal of photogrammetry and remote sensing, vol 178 (August 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)PermalinkCNN-based RGB-D salient object detection: Learn, select, and fuse / Hao Chen in International journal of computer vision, vol 129 n° 7 (July 2021)PermalinkRemote sensing image colorization using symmetrical multi-scale DCGAN in YUV color space / Min Wu in The Visual Computer, vol 37 n° 7 (July 2021)PermalinkSemantic-aware label placement for augmented reality in street view / Jianqing Jia in The Visual Computer, vol 37 n° 7 (July 2021)PermalinkSemantic unsupervised change detection of natural land cover with multitemporal object-based analysis on SAR images / Donato Amitrano in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 7 (July 2021)Permalink