Remote Sensing Image Fusion Using Hierarchical Multimodal Probabilistic Latent Semantic Analysis
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Altres documents de l'autoria: Fernandez-Beltran, Ruben; Haut, Juan M.; Paoletti, Mercedes Eugenia; Plaza, Javier; Plaza, Antonio; Pla, Filiberto
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http://dx.doi.org/10.1109/JSTARS.2018.2881342 |
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Títol
Remote Sensing Image Fusion Using Hierarchical Multimodal Probabilistic Latent Semantic AnalysisAutoria
Data de publicació
2018-11Editor
IEEECita bibliogràfica
FERNANDEZ-BELTRAN, Ruben, et al. Remote Sensing Image Fusion Using Hierarchical Multimodal Probabilistic Latent Semantic Analysis. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11.12: 4982-4993.Tipus de document
info:eu-repo/semantics/articleVersió de l'editorial
https://ieeexplore.ieee.org/document/8550740Versió
info:eu-repo/semantics/publishedVersionParaules clau / Matèries
Resum
The generative semantic nature of probabilistic topic models has recently shown encouraging results within the remote sensing image fusion field when conducting land cover categorization. However, standard topic models ... [+]
The generative semantic nature of probabilistic topic models has recently shown encouraging results within the remote sensing image fusion field when conducting land cover categorization. However, standard topic models have not yet been adapted to the inherent complexity of remotely sensed data, which eventually may limit their resulting performance. In this scenario, this paper presents a new topic-based image fusion framework, specially designed to fuse synthetic aperture radar (SAR) and multispectral imaging (MSI) data for unsupervised land cover categorization tasks. Specifically, we initially propose a hierarchical multi-modal probabilistic latent semantic analysis (HMpLSA) model that takes advantage of two different vocabulary modalities, as well as two different levels of topics, in order to effectively uncover intersensor semantic patterns. Then, we define an SAR and MSI data fusion framework based on HMpLSA in order to perform unsupervised land cover categorization. Our experiments, conducted using three different SAR and MSI data sets, reveal that the proposed approach is able to provide competitive advantages with respect to standard clustering methods and topic models, as well as several multimodal topic model variants available in the literature. [-]
Proyecto de investigación
Generalitat Valenciana (APOSTD/2017/007) ; Spanish Ministry (FPU14/02012-FPU15/02090, ESP2016-79503-C2-2-P,TIN2015-63646-C5-5-R) ; Junta de Extremadura (GR15005)Drets d'accés
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