Spectral Swin Transformer Network for Hyperspectral Image Classification

被引:12
|
作者
Liu, Baisen [1 ,2 ]
Liu, Yuanjia [1 ]
Zhang, Wulin [1 ]
Tian, Yiran [1 ]
Kong, Weili [3 ]
机构
[1] Mudanjiang Normal Univ, Dept Phys & Elect Engn, Mudanjiang 157011, Peoples R China
[2] Heilongjiang Inst Technol, Off Acad Affairs, Harbin 150001, Peoples R China
[3] Harbin Engn Univ, Dept Informat & Commun Engn, Harbin 150009, Peoples R China
关键词
hyperspectral image classification; deep learning; Swin Transformer; CNN; SVM;
D O I
10.3390/rs15153721
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Hyperspectral images are complex images that contain more spectral dimension information than ordinary images. An increasing number of HSI classification methods are using deep learning techniques to process three-dimensional data. The Vision Transformer model is gradually occupying an important position in the field of computer vision and is being used to replace the CNN structure of the network. However, it is still in the preliminary research stage in the field of HSI. In this paper, we propose using a spectral Swin Transformer network for HSI classification, providing a new approach for the HSI field. The Swin Transformer uses group attention to enhance feature representation, and the sliding window attention calculation can take into account the contextual information of different windows, which can retain the global features of HSI and improve classification results. In our experiments, we evaluated our proposed approach on several public hyperspectral datasets and compared it with several methods. The experimental results demonstrate that our proposed model achieved test accuracies of 97.46%, 99.7%, and 99.8% on the IP, SA, and PU public HSI datasets, respectively, when using the AdamW optimizer. Our approach also shows good generalization ability when applied to new datasets. Overall, our proposed approach represents a promising direction for hyperspectral image classification using deep learning techniques.
引用
收藏
页数:19
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