A Multibranch Crossover Feature Attention Network for Hyperspectral Image Classification

doi:10.3390/rs14225778

CIOMP OpenIR

	A Multibranch Crossover Feature Attention Network for Hyperspectral Image Classification
	D. X. Liu; Y. R. Wang; P. X. Liu; Q. Q. Li; H. Yang; D. B. Chen; Z. C. Liu and G. L. Han
	2022
发表期刊	Remote Sensing
卷号	14 期号:22 页码:19
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.
DOI	10.3390/rs14225778
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收录类别	sci ; ei
语种	英语
引用统计
文献类型	期刊论文
条目标识符	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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