Spectral–Spatial Feature Extraction for Hyperspectral Image Classification Using Enhanced Transformer with Large-Kernel Attention
In the hyperspectral image (HSI) classification task, every HSI pixel is labeled as a specific land cover category. Although convolutional neural network (CNN)-based HSI classification methods have made significant progress in enhancing classification performance in recent years, they still have lim...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/1/67 |
| _version_ | 1827384451593666560 |
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| author | Wen Lu Xinyu Wang Le Sun Yuhui Zheng |
| author_facet | Wen Lu Xinyu Wang Le Sun Yuhui Zheng |
| author_sort | Wen Lu |
| collection | DOAJ |
| description | In the hyperspectral image (HSI) classification task, every HSI pixel is labeled as a specific land cover category. Although convolutional neural network (CNN)-based HSI classification methods have made significant progress in enhancing classification performance in recent years, they still have limitations in acquiring deep semantic features and face the challenges of escalating computational costs with increasing network depth. In contrast, the Transformer framework excels in expressing high-level semantic features. This study introduces a novel classification network by extracting spectral–spatial features with an enhanced Transformer with Large-Kernel Attention (ETLKA). Specifically, it utilizes distinct branches of three-dimensional and two-dimensional convolutional layers to extract more diverse shallow spectral–spatial features. Additionally, a Large-Kernel Attention mechanism is incorporated and applied before the Transformer encoder to enhance feature extraction, augment comprehension of input data, reduce the impact of redundant information, and enhance the model’s robustness. Subsequently, the obtained features are input to the Transformer encoder module for feature representation and learning. Finally, a linear layer is employed to identify the first learnable token for sample label acquisition. Empirical validation confirms the outstanding classification performance of ETLKA, surpassing several advanced techniques currently in use. This research provides a robust and academically rigorous solution for HSI classification tasks, promising significant contributions in practical applications. |
| first_indexed | 2024-03-08T14:57:58Z |
| format | Article |
| id | doaj.art-0d85db05365d4f8c965efea961e10ca1 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-08T14:57:58Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-0d85db05365d4f8c965efea961e10ca12024-01-10T15:07:17ZengMDPI AGRemote Sensing2072-42922023-12-011616710.3390/rs16010067Spectral–Spatial Feature Extraction for Hyperspectral Image Classification Using Enhanced Transformer with Large-Kernel AttentionWen Lu0Xinyu Wang1Le Sun2Yuhui Zheng3The College of Computer, Qinghai Normal University, Xining 810000, ChinaSchool of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaThe College of Computer, Qinghai Normal University, Xining 810000, ChinaIn the hyperspectral image (HSI) classification task, every HSI pixel is labeled as a specific land cover category. Although convolutional neural network (CNN)-based HSI classification methods have made significant progress in enhancing classification performance in recent years, they still have limitations in acquiring deep semantic features and face the challenges of escalating computational costs with increasing network depth. In contrast, the Transformer framework excels in expressing high-level semantic features. This study introduces a novel classification network by extracting spectral–spatial features with an enhanced Transformer with Large-Kernel Attention (ETLKA). Specifically, it utilizes distinct branches of three-dimensional and two-dimensional convolutional layers to extract more diverse shallow spectral–spatial features. Additionally, a Large-Kernel Attention mechanism is incorporated and applied before the Transformer encoder to enhance feature extraction, augment comprehension of input data, reduce the impact of redundant information, and enhance the model’s robustness. Subsequently, the obtained features are input to the Transformer encoder module for feature representation and learning. Finally, a linear layer is employed to identify the first learnable token for sample label acquisition. Empirical validation confirms the outstanding classification performance of ETLKA, surpassing several advanced techniques currently in use. This research provides a robust and academically rigorous solution for HSI classification tasks, promising significant contributions in practical applications.https://www.mdpi.com/2072-4292/16/1/67CNNTransformerspectral–spatial featureHSI |
| spellingShingle | Wen Lu Xinyu Wang Le Sun Yuhui Zheng Spectral–Spatial Feature Extraction for Hyperspectral Image Classification Using Enhanced Transformer with Large-Kernel Attention Remote Sensing CNN Transformer spectral–spatial feature HSI |
| title | Spectral–Spatial Feature Extraction for Hyperspectral Image Classification Using Enhanced Transformer with Large-Kernel Attention |
| title_full | Spectral–Spatial Feature Extraction for Hyperspectral Image Classification Using Enhanced Transformer with Large-Kernel Attention |
| title_fullStr | Spectral–Spatial Feature Extraction for Hyperspectral Image Classification Using Enhanced Transformer with Large-Kernel Attention |
| title_full_unstemmed | Spectral–Spatial Feature Extraction for Hyperspectral Image Classification Using Enhanced Transformer with Large-Kernel Attention |
| title_short | Spectral–Spatial Feature Extraction for Hyperspectral Image Classification Using Enhanced Transformer with Large-Kernel Attention |
| title_sort | spectral spatial feature extraction for hyperspectral image classification using enhanced transformer with large kernel attention |
| topic | CNN Transformer spectral–spatial feature HSI |
| url | https://www.mdpi.com/2072-4292/16/1/67 |
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