Remote Sensing Image Fusion Using Hierarchical Multimodal Probabilistic Latent Semantic Analysis

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, whi...

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Bibliographic Details
Published in:IEEE journal of selected topics in applied earth observations and remote sensing Vol. 11; no. 12; pp. 4982 - 4993
Main Authors: Fernandez-Beltran, Ruben, Haut, Juan M., Paoletti, Mercedes E., Plaza, Javier, Plaza, Antonio, Pla, Filiberto
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.12.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptation models
Classification
Clustering
Computer vision
Data
Data integration
Data models
Defense industry
Frameworks
Image fusion
Image processing
Imaging techniques
Land cover
Multisensor fusion
multispectral imaging (MSI)
probabilistic latent semantic analysis (pLSA)
Probabilistic logic
Radar imaging
Remote sensing
SAR (radar)
Semantic analysis
Semantics
Synthetic aperture radar
synthetic aperture radar (SAR)
ISSN:1939-1404, 2151-1535
Online Access:Get full text
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Summary: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.
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ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2018.2881342