A Multidimensional Spectral Transformer with Channel-Wise Correlation for Hyperspectral Image Classification
Convolutional neural networks (CNNs) have been developed as an effective strategy for hyperspectral image (HSI) classification. However, the lack of feature extraction by CNN networks is due to the network failing to effectively extract global features and poor capability in distinguishing between d...
Main Authors: | Kai Zhang, Zheng Tan, Jianying Sun, Baoyu Zhu, Yuanbo Yang, Qunbo Lv |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2023-04-01
|
Series: | Applied Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/2076-3417/13/9/5482 |
Similar Items
-
Residual Group Channel and Space Attention Network for Hyperspectral Image Classification
by: Peida Wu, et al.
Published: (2020-06-01) -
Double-Branch Multi-Attention Mechanism Network for Hyperspectral Image Classification
by: Wenping Ma, et al.
Published: (2019-06-01) -
Siamese Spectral Attention With Channel Consistency for Hyperspectral Image Classification
by: Leiquan Wang, et al.
Published: (2021-01-01) -
Depth-Wise Separable Convolution Neural Network with Residual Connection for Hyperspectral Image Classification
by: Lanxue Dang, et al.
Published: (2020-10-01) -
DCTransformer: A Channel Attention Combined Discrete Cosine Transform to Extract Spatial–Spectral Feature for Hyperspectral Image Classification
by: Yuanyuan Dang, et al.
Published: (2024-02-01)