Multimodal Probabilistic Latent Semantic Analysis for Sentinel-1 and Sentinel-2 Image Fusion
- 21 June 2018
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Geoscience and Remote Sensing Letters
- Vol. 15 (9) , 1347-1351
Abstract
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.Keywords
Funding Information
- Generalitat Valenciana (APOSTD/2017/007)
- Ministerio de Educación, Cultura y Deporte (FPU14/02012-FPU15/02090, ESP2016-79503-C2-2-P)
- Consejería de Educación y Empleo, Junta de Extremadura (GR15005)
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