3DVT: Hyperspectral Image Classification Using 3D Dilated Convolution and Mean Transformer
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| Publicat a: | Photonics vol. 12, no. 2 (2025), p. 146 |
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| Autor principal: | |
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MDPI AG
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| Accés en línia: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3171182082 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2304-6732 | ||
| 024 | 7 | |a 10.3390/photonics12020146 |2 doi | |
| 035 | |a 3171182082 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231546 |2 nlm | ||
| 100 | 1 | |a Su, Xinling | |
| 245 | 1 | |a 3DVT: Hyperspectral Image Classification Using 3D Dilated Convolution and Mean Transformer | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Hyperspectral imaging and laser technology both rely on different wavelengths of light to analyze the characteristics of materials, revealing their composition, state, or structure through precise spectral data. In hyperspectral image (HSI) classification tasks, the limited number of labeled samples and the lack of feature extraction diversity often lead to suboptimal classification performance. Furthermore, traditional convolutional neural networks (CNNs) primarily focus on local features in hyperspectral data, neglecting long-range dependencies and global context. To address these challenges, this paper proposes a novel model that combines CNNs with an average pooling Vision Transformer (ViT) for hyperspectral image classification. The model utilizes three-dimensional dilated convolution and two-dimensional convolution to extract multi-scale spatial–spectral features, while ViT was employed to capture global features and long-range dependencies in the hyperspectral data. Unlike the traditional ViT encoder, which uses linear projection, our model replaces it with average pooling projection. This change enhances the extraction of local features and compensates for the ViT encoder’s limitations in local feature extraction. This hybrid approach effectively combines the local feature extraction strengths of CNNs with the long-range dependency handling capabilities of Transformers, significantly improving overall performance in hyperspectral image classification tasks. Additionally, the proposed method holds promise for the classification of fiber laser spectra, where high precision and spectral analysis are crucial for distinguishing between different fiber laser characteristics. Experimental results demonstrate that the CNN-Transformer model substantially improves classification accuracy on three benchmark hyperspectral datasets. The overall accuracies achieved on the three public datasets—IP, PU, and SV—were 99.35%, 99.31%, and 99.66%, respectively. These advancements offer potential benefits for a wide range of applications, including high-performance optical fiber sensing, laser medicine, and environmental monitoring, where accurate spectral classification is essential for the development of advanced systems in fields such as laser medicine and optical fiber technology. | |
| 653 | |a Feature extraction | ||
| 653 | |a Environmental monitoring | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Spectrum analysis | ||
| 653 | |a Image processing | ||
| 653 | |a Classification | ||
| 653 | |a Coders | ||
| 653 | |a Fiber technology | ||
| 653 | |a Accuracy | ||
| 653 | |a Datasets | ||
| 653 | |a Spectral analysis | ||
| 653 | |a Remote sensing | ||
| 653 | |a Spectral classification | ||
| 653 | |a Lasers | ||
| 653 | |a Neural networks | ||
| 653 | |a Optical fibers | ||
| 653 | |a Image classification | ||
| 653 | |a Methods | ||
| 653 | |a Wavelengths | ||
| 653 | |a Fiber lasers | ||
| 653 | |a Semantics | ||
| 653 | |a Hyperspectral imaging | ||
| 700 | 1 | |a Shao, Jingbo | |
| 773 | 0 | |t Photonics |g vol. 12, no. 2 (2025), p. 146 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3171182082/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3171182082/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3171182082/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |