Nonlinear Compressed Sensing-Based LDA Topic Model for Polarimetric SAR Image Classification

In this paper, a nonlinear compressed sensing-based LDA Topic (NCSLT) model is proposed for the classification of polarimetric synthetic aperture radar (PolSAR) images. The CS theory shows that when a signal is sparsely rendered on some basis, it can be recovered exactly by a relatively small set of...

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Bibliographic Details
Published inIEEE journal of selected topics in applied earth observations and remote sensing Vol. 7; no. 3; pp. 972 - 982
Main Authors He, Chu, Zhuo, Tong, Ou, Dan, Liu, Ming, Liao, MingSheng
Format Journal Article
LanguageEnglish
Published IEEE 01.03.2014
Subjects
Analytical models
Covariance matrices
Image classification
Manifolds
Matrix decomposition
nonlinear compressed sensing-based LDA Topic (NCSLT) model
polarimetric synthetic aperture radar (SAR)
Scattering
Semantics
Synthetic aperture radar
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Summary:In this paper, a nonlinear compressed sensing-based LDA Topic (NCSLT) model is proposed for the classification of polarimetric synthetic aperture radar (PolSAR) images. The CS theory shows that when a signal is sparsely rendered on some basis, it can be recovered exactly by a relatively small set of random measurements of the original signal. In this paper, such notion is applied to a more general case to analyze nonlinear PolSAR data. Therefore, the NCSLT model is presented with the following two objectives: to capture the nonlinear structure of PolSAR data on a manifold surface using the CS theory and to provide a generative explanation for the relationship between the image pixels and high-level complex scenes for image classification by establishing a Texture-CS-Topic model. Compared with the other traditional SAR image-classification methods, the proposed method displayed potential achievements when applied to two sets of PolSAR image data.
ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2013.2293343