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	Multi-Structure KELM With Attention Fusion Strategy for Hyperspectral Image Classification
	Sun, Le1,2; Fang, Yu1; Chen, Yuwen3; Huang, Wei4; Wu, Zebin5; Jeon, Byeungwoo6
	2022
摘要	Hyperspectral image (HSI) classification refers to accurately corresponding each pixel in an HSI to a land-cover label. Recently, the successful application of multiscale and multifeature methods has greatly improved the performance of HSI classification due to their enhanced utilization of the available spectral-spatial information. However, as the number of scales and the number of features increases, it becomes more difficult to achieve an optimal degree of fusion for multiple classifiers [e.g., kernel extreme learning machine (KELM)]. On the other hand, a limited sample size of the HSI may cause overfitting problems, which seriously affects the classification accuracy. Therefore, in this article, a novel multi-structure KELM with attention fusion strategy (MSAF-KELM) is proposed to achieve accurate fusion of multiple classifiers for effective HSI classification with ultrasmall sample rates. First, a multi-structure network is built, which combines multiple scales and multiple features to extract abundant spectral-spatial information. Second, a fast and efficient KELM is employed to enable rapid classification. Finally, a weighted self-attention fusion strategy (WSAFS) is introduced, which combines the output weights of each KELM subbranch and the self-attention mechanism to achieve an efficient fusion result on multi-structure networks. We conducted experiments on four types of HSI datasets with different evaluation methods and compared them with several classical and state-of-the-art methods, which demonstrate the excellent performance of our method on ultrasmall sample rates. The code is available at https://github.com/Fang666666/MSAF-KELM for reproducibility.
关键词	Feature extraction Training Deep learning Kernel Hyperspectral imaging Electronic mail Extreme learning machines Attention mechanisms hyperspectral image (HSI) classification kernel extreme learning machine (KELM) multifeature multiscale (MS)
DOI	10.1109/TGRS.2022.3208165
发表期刊	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN	0196-2892
卷号	60页码:17
通讯作者	Huang, Wei(hnhw235@163.com)
收录类别	SCI
WOS记录号	WOS:000864196200001
语种	英语