Spectral-spatial wave and frequency interactive transformer for hyperspectral image classification
Abstract Efficient extraction of spectral-spatial features is essential for accurate hyperspectral image (HSI) classification, where capturing both local texture and global semantic relationships is critical. While Convolutional Neural Networks (CNNs) and Transformers have shown strong capabilities...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-12489-3 |
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| _version_ | 1849763773144891392 |
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| author | Tahir Arshad Bo peng Ali Rahman Rahim khan Sajid Ullah khan Sultan Alnazi Nazik Alturki |
| author_facet | Tahir Arshad Bo peng Ali Rahman Rahim khan Sajid Ullah khan Sultan Alnazi Nazik Alturki |
| author_sort | Tahir Arshad |
| collection | DOAJ |
| description | Abstract Efficient extraction of spectral-spatial features is essential for accurate hyperspectral image (HSI) classification, where capturing both local texture and global semantic relationships is critical. While Convolutional Neural Networks (CNNs) and Transformers have shown strong capabilities in modeling local and global dependencies, most existing architectures operate directly on raw spectral-spatial inputs and lack explicit mechanisms for frequency-domain decomposition thereby overlooking potentially discriminative phase and frequency components. To address this limitation, we propose a Spectral-Spatial Wave and Frequency Interactive Transformer for HSI classification, which integrates frequency-aware and phase-aware token representations into a unified Transformer framework. Specifically, our model first employs a CNN backbone to extract shallow spectral-spatial features. These are then processed by a novel Frequency Domain Transformer Encoder, composed of two complementary branches: (i) a Spectral-Spatial Frequency Generator that extracts multiscale frequency features, and (ii) a Spectral-Spatial Wave Generator that encodes phase and amplitude characteristics as complex-valued wave tokens. A Spectral-Spatial Interaction Module fuses these components, followed by a Local-Global Modulator that refines semantic representations from multiple perspectives. Extensive experiments on five benchmark HSI datasets, demonstrate the effectiveness of our approach. The proposed model achieves state-of-the-art classification performance, with Overall Accuracies of 98.49%, 98.60%, 99.07%, 98.29%, and 97.97%, consistently outperforming existing methods. |
| format | Article |
| id | doaj-art-e564d5edb237416cb037f36176b2c281 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-e564d5edb237416cb037f36176b2c2812025-08-20T03:05:18ZengNature PortfolioScientific Reports2045-23222025-07-0115112010.1038/s41598-025-12489-3Spectral-spatial wave and frequency interactive transformer for hyperspectral image classificationTahir Arshad0Bo peng1Ali Rahman2Rahim khan3Sajid Ullah khan4Sultan Alnazi5Nazik Alturki6School of Computing and Artificial Intelligence, Southwest Jiaotong UniversitySchool of Computing and Artificial Intelligence, Southwest Jiaotong UniversitySchool of Civil Engineering, Faculty of Engineering and Physical Sciences, University of LeedsCollege of Information and Communication Engineering, Harbin Engineering UniversityDepartment of Information Systems, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz UniversityDepartment of Information Systems, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz UniversityDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman UniversityAbstract Efficient extraction of spectral-spatial features is essential for accurate hyperspectral image (HSI) classification, where capturing both local texture and global semantic relationships is critical. While Convolutional Neural Networks (CNNs) and Transformers have shown strong capabilities in modeling local and global dependencies, most existing architectures operate directly on raw spectral-spatial inputs and lack explicit mechanisms for frequency-domain decomposition thereby overlooking potentially discriminative phase and frequency components. To address this limitation, we propose a Spectral-Spatial Wave and Frequency Interactive Transformer for HSI classification, which integrates frequency-aware and phase-aware token representations into a unified Transformer framework. Specifically, our model first employs a CNN backbone to extract shallow spectral-spatial features. These are then processed by a novel Frequency Domain Transformer Encoder, composed of two complementary branches: (i) a Spectral-Spatial Frequency Generator that extracts multiscale frequency features, and (ii) a Spectral-Spatial Wave Generator that encodes phase and amplitude characteristics as complex-valued wave tokens. A Spectral-Spatial Interaction Module fuses these components, followed by a Local-Global Modulator that refines semantic representations from multiple perspectives. Extensive experiments on five benchmark HSI datasets, demonstrate the effectiveness of our approach. The proposed model achieves state-of-the-art classification performance, with Overall Accuracies of 98.49%, 98.60%, 99.07%, 98.29%, and 97.97%, consistently outperforming existing methods.https://doi.org/10.1038/s41598-025-12489-3Attention moduleConvolutional neural networkHyperspectral image classificationFrequency domainVision transformer |
| spellingShingle | Tahir Arshad Bo peng Ali Rahman Rahim khan Sajid Ullah khan Sultan Alnazi Nazik Alturki Spectral-spatial wave and frequency interactive transformer for hyperspectral image classification Scientific Reports Attention module Convolutional neural network Hyperspectral image classification Frequency domain Vision transformer |
| title | Spectral-spatial wave and frequency interactive transformer for hyperspectral image classification |
| title_full | Spectral-spatial wave and frequency interactive transformer for hyperspectral image classification |
| title_fullStr | Spectral-spatial wave and frequency interactive transformer for hyperspectral image classification |
| title_full_unstemmed | Spectral-spatial wave and frequency interactive transformer for hyperspectral image classification |
| title_short | Spectral-spatial wave and frequency interactive transformer for hyperspectral image classification |
| title_sort | spectral spatial wave and frequency interactive transformer for hyperspectral image classification |
| topic | Attention module Convolutional neural network Hyperspectral image classification Frequency domain Vision transformer |
| url | https://doi.org/10.1038/s41598-025-12489-3 |
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