Local Auxiliary Spatial–Spectral Decoupling Transformer Network for Cross-Scene Hyperspectral Image Classification
The feature-level domain alignment based on deep learning techniques has greatly improved the performance of unsupervised domain adaptation (UDA) for hyperspectral image (HSI) classification. However, most of these methods leverage convolutional neural networks to capture local features, overlooking...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11023211/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849706187751161856 |
|---|---|
| author | Qiusheng Chen Zhuoqun Fang Zhaokui Li Qian Du Shizhuo Deng Tong Jia Dongyue Chen |
| author_facet | Qiusheng Chen Zhuoqun Fang Zhaokui Li Qian Du Shizhuo Deng Tong Jia Dongyue Chen |
| author_sort | Qiusheng Chen |
| collection | DOAJ |
| description | The feature-level domain alignment based on deep learning techniques has greatly improved the performance of unsupervised domain adaptation (UDA) for hyperspectral image (HSI) classification. However, most of these methods leverage convolutional neural networks to capture local features, overlooking the comparable spatial global (SaG) and spectral global (SeG) information shared by both the source and target domains. To overcome this issue, we propose a local auxiliary spatial–spectral decoupling transformer network to ease the learning of global domain-invariant information. The SaG and SeG features of HSIs are extracted through a dual-branch design, preventing the feature coupling of different dimensions. In order to compress the model’s parameter search space, a local auxiliary global feature extraction strategy is devised. First, local prior constraints are introduced by extracting primitive features using a convolutional intra-token embedding. Next, the extraction of global spatial and spectral information from these primitive features is effectively achieved using the self-attention mechanism. Finally, a dynamic feature fusion mechanism is devised that enables the model to focus on features more conducive to transfer while suppressing irrelevant features. By using only standard adversarial domain alignment, LASDT achieves the state-of-the-art performance, demonstrating the model’s superior capability in UDA for HSI classification. |
| format | Article |
| id | doaj-art-e08376d55f4f46c9adcd53e19002198e |
| 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-e08376d55f4f46c9adcd53e19002198e2025-08-20T03:16:15ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118147841480310.1109/JSTARS.2025.357636211023211Local Auxiliary Spatial–Spectral Decoupling Transformer Network for Cross-Scene Hyperspectral Image ClassificationQiusheng Chen0https://orcid.org/0000-0003-3978-0414Zhuoqun Fang1https://orcid.org/0000-0003-1259-5470Zhaokui Li2https://orcid.org/0000-0002-5331-7664Qian Du3https://orcid.org/0000-0001-8354-7500Shizhuo Deng4https://orcid.org/0000-0002-6863-8516Tong Jia5https://orcid.org/0000-0003-1424-798XDongyue Chen6https://orcid.org/0000-0003-0673-6767College of Information Science and Engineering, Northeastern University, Shenyang, ChinaCollege of Artificial Intelligence, Shenyang Aerospace University, Shenyang, ChinaSchool of Computer Science, Shenyang Aerospace University, Shenyang, ChinaDepartment of Electrical and Computer Engineering, Mississippi State University, Starkville, MS, USACollege of Information Science and Engineering, Northeastern University, Shenyang, ChinaCollege of Information Science and Engineering, Northeastern University, Shenyang, ChinaCollege of Information Science and Engineering, Northeastern University, Shenyang, ChinaThe feature-level domain alignment based on deep learning techniques has greatly improved the performance of unsupervised domain adaptation (UDA) for hyperspectral image (HSI) classification. However, most of these methods leverage convolutional neural networks to capture local features, overlooking the comparable spatial global (SaG) and spectral global (SeG) information shared by both the source and target domains. To overcome this issue, we propose a local auxiliary spatial–spectral decoupling transformer network to ease the learning of global domain-invariant information. The SaG and SeG features of HSIs are extracted through a dual-branch design, preventing the feature coupling of different dimensions. In order to compress the model’s parameter search space, a local auxiliary global feature extraction strategy is devised. First, local prior constraints are introduced by extracting primitive features using a convolutional intra-token embedding. Next, the extraction of global spatial and spectral information from these primitive features is effectively achieved using the self-attention mechanism. Finally, a dynamic feature fusion mechanism is devised that enables the model to focus on features more conducive to transfer while suppressing irrelevant features. By using only standard adversarial domain alignment, LASDT achieves the state-of-the-art performance, demonstrating the model’s superior capability in UDA for HSI classification.https://ieeexplore.ieee.org/document/11023211/Hyperspectral image (HSI) classificationtransformerunsupervised domain adaptation (UDA) |
| spellingShingle | Qiusheng Chen Zhuoqun Fang Zhaokui Li Qian Du Shizhuo Deng Tong Jia Dongyue Chen Local Auxiliary Spatial–Spectral Decoupling Transformer Network for Cross-Scene Hyperspectral Image Classification IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Hyperspectral image (HSI) classification transformer unsupervised domain adaptation (UDA) |
| title | Local Auxiliary Spatial–Spectral Decoupling Transformer Network for Cross-Scene Hyperspectral Image Classification |
| title_full | Local Auxiliary Spatial–Spectral Decoupling Transformer Network for Cross-Scene Hyperspectral Image Classification |
| title_fullStr | Local Auxiliary Spatial–Spectral Decoupling Transformer Network for Cross-Scene Hyperspectral Image Classification |
| title_full_unstemmed | Local Auxiliary Spatial–Spectral Decoupling Transformer Network for Cross-Scene Hyperspectral Image Classification |
| title_short | Local Auxiliary Spatial–Spectral Decoupling Transformer Network for Cross-Scene Hyperspectral Image Classification |
| title_sort | local auxiliary spatial x2013 spectral decoupling transformer network for cross scene hyperspectral image classification |
| topic | Hyperspectral image (HSI) classification transformer unsupervised domain adaptation (UDA) |
| url | https://ieeexplore.ieee.org/document/11023211/ |
| work_keys_str_mv | AT qiushengchen localauxiliaryspatialx2013spectraldecouplingtransformernetworkforcrossscenehyperspectralimageclassification AT zhuoqunfang localauxiliaryspatialx2013spectraldecouplingtransformernetworkforcrossscenehyperspectralimageclassification AT zhaokuili localauxiliaryspatialx2013spectraldecouplingtransformernetworkforcrossscenehyperspectralimageclassification AT qiandu localauxiliaryspatialx2013spectraldecouplingtransformernetworkforcrossscenehyperspectralimageclassification AT shizhuodeng localauxiliaryspatialx2013spectraldecouplingtransformernetworkforcrossscenehyperspectralimageclassification AT tongjia localauxiliaryspatialx2013spectraldecouplingtransformernetworkforcrossscenehyperspectralimageclassification AT dongyuechen localauxiliaryspatialx2013spectraldecouplingtransformernetworkforcrossscenehyperspectralimageclassification |