IEEE Transactions on geoscience and remote sensing / IEEE Geoscience and remote sensing society (Etats-Unis) . vol 60 n° 11Paru le : 01/11/2022 |
[n° ou bulletin]
est un bulletin de IEEE Transactions on geoscience and remote sensing / IEEE Geoscience and remote sensing society (Etats-Unis) (1986 -)
[n° ou bulletin]
|
Dépouillements
Ajouter le résultat dans votre panierCross-guided pyramid attention-based residual hyperdense network for hyperspectral image pansharpening / Jiahui Qu in IEEE Transactions on geoscience and remote sensing, vol 60 n° 11 (November 2022)
[article]
Titre : Cross-guided pyramid attention-based residual hyperdense network for hyperspectral image pansharpening Type de document : Article/Communication Auteurs : Jiahui Qu, Auteur ; Tongzhen Zhang, Auteur ; Wenqian Dong, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 5543114 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] image hyperspectrale
[Termes IGN] image panchromatique
[Termes IGN] lissage de données
[Termes IGN] pansharpening (fusion d'images)Résumé : (auteur) Hyperspectral (HS) image pansharpening is of great importance in improving the spatial resolution for many commercial platforms and remote sensing tasks. Convolutional neural network (CNN) has recently been applied in pansharpening. However, most existing CNN-based pansharpening models followed an early-fusion/late-fusion strategy, which integrates the low-level/high-level features of panchromatic (PAN) and HS streams at the input-output of the network. It is difficult to learn more complex combinations between PAN and HS streams. This article proposes a novel end-to-end residual hyperdense pansharpening network with a cross-guided pyramid attention (called RHDcgpaNet). The overall architecture of the proposed method is a residual hyperdense network, which extends the definition of dense connections to two-stream pansharpening problem. The proposed RHDcgpaNet allows guidance from the state of the preceding layers to all the layers in- between PAN and HS streams in a feed-forward manner, significantly increasing the learning representation. A cross-guided pyramid attention is designed and embedded to the proposed residual hyperdense network to yield more useful spatial–spectral feature transfer in network. Extensive experiments on widely used datasets demonstrate that the proposed RHDcgpaNet achieves favorable performance in comparison to the state-of-the-art methods. Numéro de notice : A2022-852 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1109/TGRS.2022.3220079 Date de publication en ligne : 07/11/2022 En ligne : https://doi.org/10.1109/TGRS.2022.3220079 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102098
in IEEE Transactions on geoscience and remote sensing > vol 60 n° 11 (November 2022) . - n° 5543114[article]Graph-based leaf–wood separation method for individual trees using terrestrial lidar point clouds / Zhilin Tian in IEEE Transactions on geoscience and remote sensing, vol 60 n° 11 (November 2022)
[article]
Titre : Graph-based leaf–wood separation method for individual trees using terrestrial lidar point clouds Type de document : Article/Communication Auteurs : Zhilin Tian, Auteur ; Shihua Li, Auteur Année de publication : 2022 Article en page(s) : n° 5705111 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] bois
[Termes IGN] branche (arbre)
[Termes IGN] chemin le plus court, algorithme du
[Termes IGN] données lidar
[Termes IGN] échantillonnage de données
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] feuille (végétation)
[Termes IGN] graphe
[Termes IGN] Python (langage de programmation)
[Termes IGN] segmentation
[Termes IGN] semis de pointsRésumé : (auteur) Terrestrial light detection and ranging (lidar) is capable of resolving trees at the branch/leaf level with accurate and dense point clouds. The separation of leaf and wood components is a prerequisite for the estimation of branch/leaf-scale biophysical properties and realistic tree model reconstruction. Most existing methods have been tested on trees with similar structures; their robustness for trees of different species and sizes remains relatively unexplored. This study proposed a new graph-based leaf–wood separation (GBS) method for individual trees purely using the xyz -information of the point cloud. The GBS method fully utilized the shortest path-based features, as the shortest path can effectively reflect the structures for trees of different species and sizes. Ten types of tree data—covering tropical, temperate, and boreal species—with heights ranging from 5.4 to 43.7 m, were used to test the method performance. The mean accuracy and kappa coefficient at the point level were 94% and 0.78, respectively, and our method outperformed two other state-of-the-art methods. Through further analysis and testing, the GBS method exhibited a strong ability for detecting small and leaf-surrounded branches, and was also sufficiently robust in terms of data subsampling. Our research further demonstrated the potential of the shortest path-based features in leaf–wood separation. The entire framework was provided for use as an open-source Python package, along with our labeled validation data. Numéro de notice : A2022-853 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2022.3218603 Date de publication en ligne : 01/11/2022 En ligne : https://doi.org/10.1109/TGRS.2022.3218603 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102099
in IEEE Transactions on geoscience and remote sensing > vol 60 n° 11 (November 2022) . - n° 5705111[article]