Lightweight Spatial–Spectral Shift Module With Multihead MambaOut for Hyperspectral Image Classification
In hyperspectral images, the high dimensionality of spectral data often leads to redundant spectral information, making it difficult to extract features. Two-dimensional CNNs fail to effec-tively extract spatial and spectral information, and deploying three-dimensional CNNs on microprocessors is cha...
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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/10767195/ |
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| author | Yi Liu Yanjun Zhang Yu Guo Yunchao Li |
| author_facet | Yi Liu Yanjun Zhang Yu Guo Yunchao Li |
| author_sort | Yi Liu |
| collection | DOAJ |
| description | In hyperspectral images, the high dimensionality of spectral data often leads to redundant spectral information, making it difficult to extract features. Two-dimensional CNNs fail to effec-tively extract spatial and spectral information, and deploying three-dimensional CNNs on microprocessors is challenging as these net-works consume excessive resources. If graph convolutional networks (GCN) are adopted, most networks employ superpixel segmentation for HSI classification. However, this approach tends to overlook pixel-level features and thus fails to achieve fine classification.To efficiently extract spectral features while reducing resource consumption, we proposed the spectral shift module (SPCSM). This module extracts features by circularly shifting spectral information. It emphasizes the internal correlations within the spectral and offers the advantage of fewer parameters. Based on the SPCSM, we designed the spatial–spectral shift module (S<sup>2</sup>SM). It models and analyzes correlations between spatial and spectral data, facilitating the extraction of spatial and spectral features. Additionally, it addresses the issues of redundancy in shallow information. To enable deployment on microprocessors and effectively classify hyperspectral images, we optimized the MambaOut network by integrating the S<sup>2</sup>SM with a multihead MambaOut (LS<sup>2</sup>SM-MHMambaOut). Experiments demonstrated that our network struck a favorable balance between classification accuracy and model complexity. Therefore, it is promising to be integrated into microprocessors. |
| format | Article |
| id | doaj-art-e3d278e8a8854c84bb51dc554d046655 |
| institution | DOAJ |
| 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-e3d278e8a8854c84bb51dc554d0466552025-08-20T02:59:24ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-011892193410.1109/JSTARS.2024.350598410767195Lightweight Spatial–Spectral Shift Module With Multihead MambaOut for Hyperspectral Image ClassificationYi Liu0https://orcid.org/0009-0003-5463-4711Yanjun Zhang1https://orcid.org/0009-0006-9947-8881Yu Guo2Yunchao Li3State Key Laboratory of Dynamic Measurement Techno-logy, North University of China, Yaiyuan, ChinaState Key Laboratory of Dynamic Measurement Technology, North University of China, Yaiyuan, ChinaBeijing Electro-mechanical Engineering Institute, Beijing, ChinaNorinco Group Test and Measuring Academy, Xi'an, ChinaIn hyperspectral images, the high dimensionality of spectral data often leads to redundant spectral information, making it difficult to extract features. Two-dimensional CNNs fail to effec-tively extract spatial and spectral information, and deploying three-dimensional CNNs on microprocessors is challenging as these net-works consume excessive resources. If graph convolutional networks (GCN) are adopted, most networks employ superpixel segmentation for HSI classification. However, this approach tends to overlook pixel-level features and thus fails to achieve fine classification.To efficiently extract spectral features while reducing resource consumption, we proposed the spectral shift module (SPCSM). This module extracts features by circularly shifting spectral information. It emphasizes the internal correlations within the spectral and offers the advantage of fewer parameters. Based on the SPCSM, we designed the spatial–spectral shift module (S<sup>2</sup>SM). It models and analyzes correlations between spatial and spectral data, facilitating the extraction of spatial and spectral features. Additionally, it addresses the issues of redundancy in shallow information. To enable deployment on microprocessors and effectively classify hyperspectral images, we optimized the MambaOut network by integrating the S<sup>2</sup>SM with a multihead MambaOut (LS<sup>2</sup>SM-MHMambaOut). Experiments demonstrated that our network struck a favorable balance between classification accuracy and model complexity. Therefore, it is promising to be integrated into microprocessors.https://ieeexplore.ieee.org/document/10767195/Hyperspectral images (HSI)MambaOutspatial–spectral shift module |
| spellingShingle | Yi Liu Yanjun Zhang Yu Guo Yunchao Li Lightweight Spatial–Spectral Shift Module With Multihead MambaOut for Hyperspectral Image Classification IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Hyperspectral images (HSI) MambaOut spatial–spectral shift module |
| title | Lightweight Spatial–Spectral Shift Module With Multihead MambaOut for Hyperspectral Image Classification |
| title_full | Lightweight Spatial–Spectral Shift Module With Multihead MambaOut for Hyperspectral Image Classification |
| title_fullStr | Lightweight Spatial–Spectral Shift Module With Multihead MambaOut for Hyperspectral Image Classification |
| title_full_unstemmed | Lightweight Spatial–Spectral Shift Module With Multihead MambaOut for Hyperspectral Image Classification |
| title_short | Lightweight Spatial–Spectral Shift Module With Multihead MambaOut for Hyperspectral Image Classification |
| title_sort | lightweight spatial x2013 spectral shift module with multihead mambaout for hyperspectral image classification |
| topic | Hyperspectral images (HSI) MambaOut spatial–spectral shift module |
| url | https://ieeexplore.ieee.org/document/10767195/ |
| work_keys_str_mv | AT yiliu lightweightspatialx2013spectralshiftmodulewithmultiheadmambaoutforhyperspectralimageclassification AT yanjunzhang lightweightspatialx2013spectralshiftmodulewithmultiheadmambaoutforhyperspectralimageclassification AT yuguo lightweightspatialx2013spectralshiftmodulewithmultiheadmambaoutforhyperspectralimageclassification AT yunchaoli lightweightspatialx2013spectralshiftmodulewithmultiheadmambaoutforhyperspectralimageclassification |