A Multibranch Crossover Feature Attention Network for Hyperspectral Image Classification
文献类型：期刊论文
| 作者 | D. X. Liu; Y. R. Wang; P. X. Liu; Q. Q. Li; H. Yang; D. B. Chen; Z. C. Liu and G. L. Han |
| 刊名 | Remote Sensing
 |
| 出版日期 | 2022 |
| 卷号 | 14期号:22页码:19 |
| DOI | 10.3390/rs14225778 |
| 英文摘要 | Recently, hyperspectral image (HSI) classification methods based on convolutional neural networks (CNN) have shown impressive performance. However, HSI classification still faces two challenging problems: the first challenge is that most existing classification approaches only focus on exploiting the fixed-scale convolutional kernels to extract spectral-spatial features, which leads to underutilization of information; the second challenge is that HSI contains a large amount of redundant information and noise, to a certain extent, which influences the classification performance of CNN. In order to tackle the above problems, this article proposes a multibranch crossover feature attention network (MCFANet) for HSI classification. The MCFANet involves two primary submodules: a cross feature extraction module (CFEM) and rearranged attention module (RAM). The former is devised to capture joint spectral-spatial features at different convolutional layers, scales and branches, which can increase the diversity and complementarity of spectral-spatial features, while the latter is constructed to spontaneously concentrate on recalibrating spatial-wise and spectral-wise feature responses, meanwhile exploit the shifted cascade operation to rearrange the obtained attention-enhanced features to dispel redundant information and noise, and thus, boost the classification performance. Compared with the state-of-the-art classification methods, massive experiments on four benchmark datasets demonstrate the meliority of our presented method. |
| URL标识 | 查看原文 |
| 语种 | 英语 |
| 源URL | [http://ir.ciomp.ac.cn/handle/181722/66865]  |
| 专题 | 中国科学院长春光学精密机械与物理研究所 |
| 推荐引用方式 GB/T 7714 | D. X. Liu,Y. R. Wang,P. X. Liu,et al. A Multibranch Crossover Feature Attention Network for Hyperspectral Image Classification[J]. Remote Sensing,2022,14(22):19. |
| APA | D. X. Liu.,Y. R. Wang.,P. X. Liu.,Q. Q. Li.,H. Yang.,...&Z. C. Liu and G. L. Han.(2022).A Multibranch Crossover Feature Attention Network for Hyperspectral Image Classification.Remote Sensing,14(22),19. |
| MLA | D. X. Liu,et al."A Multibranch Crossover Feature Attention Network for Hyperspectral Image Classification".Remote Sensing 14.22(2022):19. |
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