2024-03-29T06:30:21Zhttps://repositori.uji.es/oai/requestoai:repositori.uji.es:10234/1774432022-11-08T14:56:55Zcom_10234_43662com_10234_9col_10234_43643
Repositori UJI
author
Fernandez-Beltran, Ruben
author
Haut, Juan M.
author
Paoletti, Mercedes Eugenia
author
Plaza, Javier
author
Plaza, Antonio
author
Pla, Filiberto
2018-11-13T13:12:01Z
2018-11-13T13:12:01Z
2018-09
FERNANDEZ-BELTRAN, Ruben, et al. Multimodal Probabilistic Latent Semantic Analysis for Sentinel-1 and Sentinel-2 Image Fusion. IEEE Geoscience and Remote Sensing Letters, 2018, 15.9: 1347-1351.
http://hdl.handle.net/10234/177443
http://dx.doi.org/10.1109/LGRS.2018.2843886
Probabilistic topic models have recently shown a great potential in the remote sensing image fusion field, which is particularly helpful in land-cover categorization tasks. This letter first studies the application of probabilistic latent semantic analysis (pLSA) and latent Dirichlet allocation to remote sensing synthetic aperture radar (SAR) and multispectral imaging (MSI) unsupervised land-cover categorization. Then, a novel pLSA-based image fusion approach is presented, which pursues to uncover multimodal feature patterns from SAR and MSI data in order to effectively fuse and categorize Sentinel-1 and Sentinel-2 remotely sensed data. Experiments conducted over two different data sets reveal the advantages of the proposed approach for unsupervised land-cover categorization tasks.
eng
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image fusion
probabilistic latent semantic analysis
land cover categorization
Sentinel-1
Sentinel-2
Multimodal Probabilistic Latent Semantic Analysis for Sentinel-1 and Sentinel-2 Image Fusion
info:eu-repo/semantics/article
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IEEE_GEOSCIENCE_AND_REMOTE_SENSING_LETTERS.pdf.txt