Spatial-spectral classification of hyperspectral images: a deep learning framework with Markov random fields based modeling

Chunmei Qing, Jiawei Ruan, Xiangmin Xu, Jinchang Ren, Jaime Zabalza

Research output: Contribution to journalSpecial issue

4 Citations (Scopus)

Abstract

For spatial-spectral classification of hyperspectral images (HSI), a deep learning framework is proposed in this paper, which consists of convolutional neural networks (CNN) and Markov random fields (MRF). Firstly, a CNN model to learn the deep spectral feature from the HSI is built and the class posterior probability distribution is estimated. The CNN with a dropout layer can relieve the overfitting in classification. The CNN is utilized as a pixel-classifier, so it only works in the spectral domain. Then, the spatial information will be encoded by MRF-based multilevel logistic (MLL) prior for regularizing the classification. To derive the correlation of both spectral and spatial features for improving algorithm performance, the marginal probability distribution in HSI is learned using MRF-based loopy belief propagation (LBP). In comparison with several state-of-the-art approaches for data classification on 3 publicly available HSI datasets, experimental results have demonstrated the superior performance of the proposed methodology.
LanguageEnglish
Pages235-245
Number of pages11
JournalIET Image Processing
Volume13
Issue number2
Early online date11 Sep 2018
DOIs
Publication statusPublished - 25 Feb 2019

Fingerprint

Neural networks
Probability distributions
Logistics
Classifiers
Pixels
Deep learning

Keywords

  • hyperspectral image
  • spatial-spectral classification
  • convolutional neural networks
  • Markov random fields
  • loopy belief propagation

Cite this

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title = "Spatial-spectral classification of hyperspectral images: a deep learning framework with Markov random fields based modeling",
abstract = "For spatial-spectral classification of hyperspectral images (HSI), a deep learning framework is proposed in this paper, which consists of convolutional neural networks (CNN) and Markov random fields (MRF). Firstly, a CNN model to learn the deep spectral feature from the HSI is built and the class posterior probability distribution is estimated. The CNN with a dropout layer can relieve the overfitting in classification. The CNN is utilized as a pixel-classifier, so it only works in the spectral domain. Then, the spatial information will be encoded by MRF-based multilevel logistic (MLL) prior for regularizing the classification. To derive the correlation of both spectral and spatial features for improving algorithm performance, the marginal probability distribution in HSI is learned using MRF-based loopy belief propagation (LBP). In comparison with several state-of-the-art approaches for data classification on 3 publicly available HSI datasets, experimental results have demonstrated the superior performance of the proposed methodology.",
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Spatial-spectral classification of hyperspectral images : a deep learning framework with Markov random fields based modeling. / Qing, Chunmei; Ruan, Jiawei; Xu, Xiangmin; Ren, Jinchang; Zabalza, Jaime.

In: IET Image Processing, Vol. 13, No. 2, 25.02.2019, p. 235-245.

Research output: Contribution to journalSpecial issue

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AU - Xu, Xiangmin

AU - Ren, Jinchang

AU - Zabalza, Jaime

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N2 - For spatial-spectral classification of hyperspectral images (HSI), a deep learning framework is proposed in this paper, which consists of convolutional neural networks (CNN) and Markov random fields (MRF). Firstly, a CNN model to learn the deep spectral feature from the HSI is built and the class posterior probability distribution is estimated. The CNN with a dropout layer can relieve the overfitting in classification. The CNN is utilized as a pixel-classifier, so it only works in the spectral domain. Then, the spatial information will be encoded by MRF-based multilevel logistic (MLL) prior for regularizing the classification. To derive the correlation of both spectral and spatial features for improving algorithm performance, the marginal probability distribution in HSI is learned using MRF-based loopy belief propagation (LBP). In comparison with several state-of-the-art approaches for data classification on 3 publicly available HSI datasets, experimental results have demonstrated the superior performance of the proposed methodology.

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