SSANet: An Adaptive Spectral-Spatial Attention Autoencoder Network for Hyperspectral Unmixing

doi:10.3390/rs15082070

	SSANet: An Adaptive Spectral-Spatial Attention Autoencoder Network for Hyperspectral Unmixing
	Wang, Jie 1; Xu, Jindong 1; Chong, Qianpeng 1; Liu, Zhaowei 1; Yan, Weiqing 1; Xing, Haihua 2; Xing, Qianguo3 ; Ni, Mengying 1
发表期刊	REMOTE SENSING
	2023-04-01
卷号	15 期号:8 页码:21
关键词	hyperspectral unmixing spectral-spatial attention mechanism deep learning autoencoder
DOI	10.3390/rs15082070
通讯作者	Ni, Mengying(nimengying@ytu.edu.cn)
英文摘要	Convolutional neural-network-based autoencoders, which can integrate the spatial correlation between pixels well, have been broadly used for hyperspectral unmixing and obtained excellent performance. Nevertheless, these methods are hindered in their performance by the fact that they treat all spectral bands and spatial information equally in the unmixing procedure. In this article, we propose an adaptive spectral-spatial attention autoencoder network, called SSANet, to solve the mixing pixel problem of the hyperspectral image. First, we design an adaptive spectral-spatial attention module, which refines spectral-spatial features by sequentially superimposing the spectral attention module and spatial attention module. The spectral attention module is built to select useful spectral bands, and the spatial attention module is designed to filter spatial information. Second, SSANet exploits the geometric properties of endmembers in the hyperspectral image while considering abundance sparsity. We significantly improve the endmember and abundance results by introducing minimum volume and sparsity regularization terms into the loss function. We evaluate the proposed SSANet on one synthetic dataset and four real hyperspectral scenes, i.e., Samson, Jasper Ridge, Houston, and Urban. The results indicate that the proposed SSANet achieved competitive unmixing results compared with several conventional and advanced unmixing approaches with respect to the root mean square error and spectral angle distance.
收录类别	SCI
语种	英语
关键词[WOS]	SPARSE COMPONENT ANALYSIS
研究领域[WOS]	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000977424900001
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.yic.ac.cn/handle/133337/32900
专题	中国科学院海岸带环境过程与生态修复重点实验室中国科学院海岸带环境过程与生态修复重点实验室_海岸带信息集成与战略规划研究中心
通讯作者	Ni, Mengying
作者单位	1.Yantai Univ, Sch Comp & Control Engn, Yantai 264005, Peoples R China 2.Hainan Normal Univ, Sch Informat Sci & Technol, Haikou 571158, Peoples R China 3.Chinese Acad Sci, Yantai Inst Coastal Zone Res, Yantai 264003, Peoples R China
推荐引用方式 GB/T 7714	Wang, Jie,Xu, Jindong,Chong, Qianpeng,et al. SSANet: An Adaptive Spectral-Spatial Attention Autoencoder Network for Hyperspectral Unmixing[J]. REMOTE SENSING,2023,15(8):21.
APA	Wang, Jie.,Xu, Jindong.,Chong, Qianpeng.,Liu, Zhaowei.,Yan, Weiqing.,...&Ni, Mengying.(2023).SSANet: An Adaptive Spectral-Spatial Attention Autoencoder Network for Hyperspectral Unmixing.REMOTE SENSING,15(8),21.
MLA	Wang, Jie,et al."SSANet: An Adaptive Spectral-Spatial Attention Autoencoder Network for Hyperspectral Unmixing".REMOTE SENSING 15.8(2023):21.