Prompt-Gated Transformer with Spatial–Spectral Enhancement for Hyperspectral Image Classification
Hyperspectral image (HSI) classification is an important task in the field of remote sensing, with far-reaching practical significance. Most Convolutional Neural Networks (CNNs) only focus on local spatial features and ignore global spectral dependencies, making it difficult to completely extract sp...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-08-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/15/2705 |
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| author | Ruimin Han Shuli Cheng Shuoshuo Li Tingjie Liu |
| author_facet | Ruimin Han Shuli Cheng Shuoshuo Li Tingjie Liu |
| author_sort | Ruimin Han |
| collection | DOAJ |
| description | Hyperspectral image (HSI) classification is an important task in the field of remote sensing, with far-reaching practical significance. Most Convolutional Neural Networks (CNNs) only focus on local spatial features and ignore global spectral dependencies, making it difficult to completely extract spectral information in HSI. In contrast, Vision Transformers (ViTs) are widely used in HSI due to their superior feature extraction capabilities. However, existing Transformer models have challenges in achieving spectral–spatial feature fusion and maintaining local structural consistency, making it difficult to strike a balance between global modeling capabilities and local representation. To this end, we propose a Prompt-Gated Transformer with a Spatial–Spectral Enhancement (PGTSEFormer) network, which includes a Channel Hybrid Positional Attention Module (CHPA) and Prompt Cross-Former (PCFormer). The CHPA module adopts a dual-branch architecture to concurrently capture spectral and spatial positional attention, thereby enhancing the model’s discriminative capacity for complex feature categories through adaptive weight fusion. PCFormer introduces a Prompt-Gated mechanism and grouping strategy to effectively model cross-regional contextual information, while maintaining local consistency, which significantly enhances the ability for long-distance dependent modeling. Experiments were conducted on five HSI datasets and the results showed that overall accuracies of 97.91%, 98.74%, 99.48%, 99.18%, and 92.57% were obtained on the Indian pines, Salians, Botswana, WHU-Hi-LongKou, and WHU-Hi-HongHu datasets. The experimental results show the effectiveness of our proposed approach. |
| format | Article |
| id | doaj-art-588b9afc4e1d4509a8fe2f8d377f8795 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-588b9afc4e1d4509a8fe2f8d377f87952025-08-20T03:36:22ZengMDPI AGRemote Sensing2072-42922025-08-011715270510.3390/rs17152705Prompt-Gated Transformer with Spatial–Spectral Enhancement for Hyperspectral Image ClassificationRuimin Han0Shuli Cheng1Shuoshuo Li2Tingjie Liu3School of Computer Science and Technology, Xinjiang University, Urumqi 830046, ChinaSchool of Computer Science and Technology, Xinjiang University, Urumqi 830046, ChinaSchool of Computer Science and Technology, Xinjiang University, Urumqi 830046, ChinaSchool of Computer Science and Technology, Xinjiang University, Urumqi 830046, ChinaHyperspectral image (HSI) classification is an important task in the field of remote sensing, with far-reaching practical significance. Most Convolutional Neural Networks (CNNs) only focus on local spatial features and ignore global spectral dependencies, making it difficult to completely extract spectral information in HSI. In contrast, Vision Transformers (ViTs) are widely used in HSI due to their superior feature extraction capabilities. However, existing Transformer models have challenges in achieving spectral–spatial feature fusion and maintaining local structural consistency, making it difficult to strike a balance between global modeling capabilities and local representation. To this end, we propose a Prompt-Gated Transformer with a Spatial–Spectral Enhancement (PGTSEFormer) network, which includes a Channel Hybrid Positional Attention Module (CHPA) and Prompt Cross-Former (PCFormer). The CHPA module adopts a dual-branch architecture to concurrently capture spectral and spatial positional attention, thereby enhancing the model’s discriminative capacity for complex feature categories through adaptive weight fusion. PCFormer introduces a Prompt-Gated mechanism and grouping strategy to effectively model cross-regional contextual information, while maintaining local consistency, which significantly enhances the ability for long-distance dependent modeling. Experiments were conducted on five HSI datasets and the results showed that overall accuracies of 97.91%, 98.74%, 99.48%, 99.18%, and 92.57% were obtained on the Indian pines, Salians, Botswana, WHU-Hi-LongKou, and WHU-Hi-HongHu datasets. The experimental results show the effectiveness of our proposed approach.https://www.mdpi.com/2072-4292/17/15/2705hyperspectral image classificationconvolutional neural networksvision transformerprompt-gated |
| spellingShingle | Ruimin Han Shuli Cheng Shuoshuo Li Tingjie Liu Prompt-Gated Transformer with Spatial–Spectral Enhancement for Hyperspectral Image Classification Remote Sensing hyperspectral image classification convolutional neural networks vision transformer prompt-gated |
| title | Prompt-Gated Transformer with Spatial–Spectral Enhancement for Hyperspectral Image Classification |
| title_full | Prompt-Gated Transformer with Spatial–Spectral Enhancement for Hyperspectral Image Classification |
| title_fullStr | Prompt-Gated Transformer with Spatial–Spectral Enhancement for Hyperspectral Image Classification |
| title_full_unstemmed | Prompt-Gated Transformer with Spatial–Spectral Enhancement for Hyperspectral Image Classification |
| title_short | Prompt-Gated Transformer with Spatial–Spectral Enhancement for Hyperspectral Image Classification |
| title_sort | prompt gated transformer with spatial spectral enhancement for hyperspectral image classification |
| topic | hyperspectral image classification convolutional neural networks vision transformer prompt-gated |
| url | https://www.mdpi.com/2072-4292/17/15/2705 |
| work_keys_str_mv | AT ruiminhan promptgatedtransformerwithspatialspectralenhancementforhyperspectralimageclassification AT shulicheng promptgatedtransformerwithspatialspectralenhancementforhyperspectralimageclassification AT shuoshuoli promptgatedtransformerwithspatialspectralenhancementforhyperspectralimageclassification AT tingjieliu promptgatedtransformerwithspatialspectralenhancementforhyperspectralimageclassification |