Hyperspectral Image Classification Using Spectral-Spatial Dual Random Fields With Gaussian and Markov Processes
This article presents a novel hyperspectral image (HSI) classification approach that integrates the sparse inducing variational Gaussian process (SIVGP) with a spatially adaptive Markov random field (SAMRF), termed G-MDRF. Variational inference is employed to obtain a sparse approximation of the pos...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10836880/ |
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| author | Yaqiu Zhang Lizhi Liu Xinnian Yang |
| author_facet | Yaqiu Zhang Lizhi Liu Xinnian Yang |
| author_sort | Yaqiu Zhang |
| collection | DOAJ |
| description | This article presents a novel hyperspectral image (HSI) classification approach that integrates the sparse inducing variational Gaussian process (SIVGP) with a spatially adaptive Markov random field (SAMRF), termed G-MDRF. Variational inference is employed to obtain a sparse approximation of the posterior distribution, modeling the spectral field within the latent function space. Subsequently, SAMRF is utilized to model the spatial prior within the function space, while the alternating direction method of multipliers (ADMM) is employed to enhance computational efficiency. Experimental results on three datasets with varying complexity show that the proposed algorithm improves computational efficiency by approximately 152 times and accuracy by about 7%–26% compared to the current popular Gaussian process methods. Compared to classical random field methods, G-MDRF rapidly achieves a convergent solution with only one ten-thousandth to one hundred-thousandth of the iterations, improving accuracy by about 5%–18%. Particularly, when the number of classes in the dataset increases and the scene becomes more complex, the proposed method demonstrates a greater advantage in both computational efficiency and classification accuracy compared to existing methods. |
| format | Article |
| id | doaj-art-ecd738aeed31400a8e403f3c7438748d |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-ecd738aeed31400a8e403f3c7438748d2025-01-31T00:00:31ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01184199421210.1109/JSTARS.2025.352811510836880Hyperspectral Image Classification Using Spectral-Spatial Dual Random Fields With Gaussian and Markov ProcessesYaqiu Zhang0https://orcid.org/0000-0002-0819-0799Lizhi Liu1Xinnian Yang2https://orcid.org/0009-0002-9493-0980School of Electrical and Information Engineering, Hubei University of Automotive Technology, Shiyan, ChinaCollege of Landscape and Forestry, Tarim University, Xinjiang, ChinaSchool of Intelligent Connected Vehicle, Hubei University of Automotive Technology, Shiyan, ChinaThis article presents a novel hyperspectral image (HSI) classification approach that integrates the sparse inducing variational Gaussian process (SIVGP) with a spatially adaptive Markov random field (SAMRF), termed G-MDRF. Variational inference is employed to obtain a sparse approximation of the posterior distribution, modeling the spectral field within the latent function space. Subsequently, SAMRF is utilized to model the spatial prior within the function space, while the alternating direction method of multipliers (ADMM) is employed to enhance computational efficiency. Experimental results on three datasets with varying complexity show that the proposed algorithm improves computational efficiency by approximately 152 times and accuracy by about 7%–26% compared to the current popular Gaussian process methods. Compared to classical random field methods, G-MDRF rapidly achieves a convergent solution with only one ten-thousandth to one hundred-thousandth of the iterations, improving accuracy by about 5%–18%. Particularly, when the number of classes in the dataset increases and the scene becomes more complex, the proposed method demonstrates a greater advantage in both computational efficiency and classification accuracy compared to existing methods.https://ieeexplore.ieee.org/document/10836880/Gaussian processes (GPs)hyperspectral image (HSI) classificationMarkov random field (MRF)stochastic processes |
| spellingShingle | Yaqiu Zhang Lizhi Liu Xinnian Yang Hyperspectral Image Classification Using Spectral-Spatial Dual Random Fields With Gaussian and Markov Processes IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Gaussian processes (GPs) hyperspectral image (HSI) classification Markov random field (MRF) stochastic processes |
| title | Hyperspectral Image Classification Using Spectral-Spatial Dual Random Fields With Gaussian and Markov Processes |
| title_full | Hyperspectral Image Classification Using Spectral-Spatial Dual Random Fields With Gaussian and Markov Processes |
| title_fullStr | Hyperspectral Image Classification Using Spectral-Spatial Dual Random Fields With Gaussian and Markov Processes |
| title_full_unstemmed | Hyperspectral Image Classification Using Spectral-Spatial Dual Random Fields With Gaussian and Markov Processes |
| title_short | Hyperspectral Image Classification Using Spectral-Spatial Dual Random Fields With Gaussian and Markov Processes |
| title_sort | hyperspectral image classification using spectral spatial dual random fields with gaussian and markov processes |
| topic | Gaussian processes (GPs) hyperspectral image (HSI) classification Markov random field (MRF) stochastic processes |
| url | https://ieeexplore.ieee.org/document/10836880/ |
| work_keys_str_mv | AT yaqiuzhang hyperspectralimageclassificationusingspectralspatialdualrandomfieldswithgaussianandmarkovprocesses AT lizhiliu hyperspectralimageclassificationusingspectralspatialdualrandomfieldswithgaussianandmarkovprocesses AT xinnianyang hyperspectralimageclassificationusingspectralspatialdualrandomfieldswithgaussianandmarkovprocesses |