Tensor-Based Few-Shot Learning for Cross-Domain Hyperspectral Image Classification
Few-shot learning (FSL) is an effective solution for cross-domain hyperspectral image (HSI) classification, which could address the limited labeled samples of the target domain. Current FSL methods mostly utilize the 3D-CNN to transform the spatial and spectral information into a single feature to m...
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MDPI AG
2024-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/22/4149 |
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| author | Haojin Tang Xiaofei Yang Dong Tang Yiru Dong Li Zhang Weixin Xie |
| author_facet | Haojin Tang Xiaofei Yang Dong Tang Yiru Dong Li Zhang Weixin Xie |
| author_sort | Haojin Tang |
| collection | DOAJ |
| description | Few-shot learning (FSL) is an effective solution for cross-domain hyperspectral image (HSI) classification, which could address the limited labeled samples of the target domain. Current FSL methods mostly utilize the 3D-CNN to transform the spatial and spectral information into a single feature to model an HSI, which means that spatial and spectral information are treated equally in the feature-modeling process. However, spectral information is considered to be more domain-invariant than spatial information. Treating the spatial and spectral information equally may result in parameter redundancy and undesirable cross-domain classification performance. In this paper, we attempt to use tensor mathematics for modeling the HSI and propose a novel few-shot learning method, called tensor-based few-shot Learning (TFSL) for cross-domain HSI classification, which aims to guide the model to focus on the extraction of domain-invariant spectral dependencies. Specifically, we first propose a spatial–spectral tensor decomposition (SSTD) model to provide a mathematical explanation of the input HSI, representing the local spatial–spectral information as 1D and 2D local tensors to reduce the data redundancy. Additionally, a tensor-based hybrid two-stream (THT) model is proposed for extracting the domain-invariant spatial–spectral tensor feature by using 1D-CNN and 2D-CNN. Furthermore, we adopt a 1D-CNN tensor feature enhancement block to enhance the spectral feature of hybrid two-stream tensors and guide the THT model to concentrate on the modeling of spectral dependencies. Finally, the proposed TFSL is evaluated on four public HSI datasets, and the extensive experimental results demonstrate that the proposed TFSL significantly outperforms other advanced FSL methods. |
| format | Article |
| id | doaj-art-9d25dff679534d3ba99c22853f90c438 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-9d25dff679534d3ba99c22853f90c4382024-11-26T18:19:43ZengMDPI AGRemote Sensing2072-42922024-11-011622414910.3390/rs16224149Tensor-Based Few-Shot Learning for Cross-Domain Hyperspectral Image ClassificationHaojin Tang0Xiaofei Yang1Dong Tang2Yiru Dong3Li Zhang4Weixin Xie5School of Electronic and Communication Engineering, Guangzhou University, Guangzhou 511370, ChinaSchool of Electronic and Communication Engineering, Guangzhou University, Guangzhou 511370, ChinaSchool of Electronic and Communication Engineering, Guangzhou University, Guangzhou 511370, ChinaSchool of Electronic and Communication Engineering, Guangzhou University, Guangzhou 511370, ChinaGuangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen 518060, ChinaGuangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen 518060, ChinaFew-shot learning (FSL) is an effective solution for cross-domain hyperspectral image (HSI) classification, which could address the limited labeled samples of the target domain. Current FSL methods mostly utilize the 3D-CNN to transform the spatial and spectral information into a single feature to model an HSI, which means that spatial and spectral information are treated equally in the feature-modeling process. However, spectral information is considered to be more domain-invariant than spatial information. Treating the spatial and spectral information equally may result in parameter redundancy and undesirable cross-domain classification performance. In this paper, we attempt to use tensor mathematics for modeling the HSI and propose a novel few-shot learning method, called tensor-based few-shot Learning (TFSL) for cross-domain HSI classification, which aims to guide the model to focus on the extraction of domain-invariant spectral dependencies. Specifically, we first propose a spatial–spectral tensor decomposition (SSTD) model to provide a mathematical explanation of the input HSI, representing the local spatial–spectral information as 1D and 2D local tensors to reduce the data redundancy. Additionally, a tensor-based hybrid two-stream (THT) model is proposed for extracting the domain-invariant spatial–spectral tensor feature by using 1D-CNN and 2D-CNN. Furthermore, we adopt a 1D-CNN tensor feature enhancement block to enhance the spectral feature of hybrid two-stream tensors and guide the THT model to concentrate on the modeling of spectral dependencies. Finally, the proposed TFSL is evaluated on four public HSI datasets, and the extensive experimental results demonstrate that the proposed TFSL significantly outperforms other advanced FSL methods.https://www.mdpi.com/2072-4292/16/22/4149few-shot learningcross-domain hyperspectral image classificationtensor mathematics |
| spellingShingle | Haojin Tang Xiaofei Yang Dong Tang Yiru Dong Li Zhang Weixin Xie Tensor-Based Few-Shot Learning for Cross-Domain Hyperspectral Image Classification Remote Sensing few-shot learning cross-domain hyperspectral image classification tensor mathematics |
| title | Tensor-Based Few-Shot Learning for Cross-Domain Hyperspectral Image Classification |
| title_full | Tensor-Based Few-Shot Learning for Cross-Domain Hyperspectral Image Classification |
| title_fullStr | Tensor-Based Few-Shot Learning for Cross-Domain Hyperspectral Image Classification |
| title_full_unstemmed | Tensor-Based Few-Shot Learning for Cross-Domain Hyperspectral Image Classification |
| title_short | Tensor-Based Few-Shot Learning for Cross-Domain Hyperspectral Image Classification |
| title_sort | tensor based few shot learning for cross domain hyperspectral image classification |
| topic | few-shot learning cross-domain hyperspectral image classification tensor mathematics |
| url | https://www.mdpi.com/2072-4292/16/22/4149 |
| work_keys_str_mv | AT haojintang tensorbasedfewshotlearningforcrossdomainhyperspectralimageclassification AT xiaofeiyang tensorbasedfewshotlearningforcrossdomainhyperspectralimageclassification AT dongtang tensorbasedfewshotlearningforcrossdomainhyperspectralimageclassification AT yirudong tensorbasedfewshotlearningforcrossdomainhyperspectralimageclassification AT lizhang tensorbasedfewshotlearningforcrossdomainhyperspectralimageclassification AT weixinxie tensorbasedfewshotlearningforcrossdomainhyperspectralimageclassification |