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自然资源遥感  2022, Vol. 34 Issue (1): 43-52    DOI: 10.6046/zrzyyg.2021115
     技术方法 本期目录 | 过刊浏览 | 高级检索 |
多感受野特征与空谱注意力结合的高光谱图像超分辨率算法
曲海成(), 王雅萱(), 申磊
辽宁工程技术大学软件学院,葫芦岛 125105
Hyperspectral super-resolution combining multi-receptive field features with spectral-spatial attention
QU Haicheng(), WAND Yaxuan(), SHEN Lei
College of Software, Liaoning Technical University, Huludao 125105, China
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摘要 

针对高光谱图像超分辨率过程中,图像细节信息容易丢失的问题,提出多感受野特征与空谱注意力结合的高光谱图像超分辨率算法,该算法充分利用高光谱图像中的高频信息与低频信息,减少图像细节信息丢失,提升了高光谱图像超分辨率效果。首先,在特征提取阶段采用不同大小卷积核的卷积,获取到多尺度感受野特征,更好地提取低分辨率图像中的高频信息与低频信息,有助于保留原始图像的特征信息; 然后,把获取到图像特征,经过“空间-光谱”结合的注意力机制增强,利用光谱维信息辅助空间维特征重建; 最后,把每组的特征融合,通过像素级反卷积层缓解棋盘格效应,输出清晰的高分辨率图像。实验结果表明: 提出的多感受野特征与空谱注意力结合的超分辨率算法在Chikusei和Pavia center scene这2个公开数据集上峰值信噪比分别达到了39.869 7和31.942 2,结构相似度分别达到了0.937 6和0.878 6,与最新超分辨率算法比较有明显的性能优势。该文提出的算法,结合了多感受野特征提取模块和空谱结合注意力模块的优势,超分辨率后的图像细节特征有了明显的改善。

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曲海成
王雅萱
申磊
关键词 高光谱图像图像超分辨率多感受野特征提取注意力机制    
Abstract

To address the problem that image details are liable to be lost in the process of hyperspectral super-resolution, this study proposed a hyperspectral super-resolution algorithm that combines multi-receptive field features and spectral-spatial attention. By fully using the high- and low-frequency information in hyperspectral images, this algorithm reduces the loss of image details and improves the hyperspectral super-resolution effects. First, in the feature extraction stage, convolution with different sizes of convolutional kernels is used to obtain multi-scale receptive field features. This assists in extracting more high- and low-frequency information from low-resolution images, thus retaining the features of original images. Then, the acquired image features are enhanced by the spatial-spectral attention mechanism, and the reconstruction of spatial-dimension features is conducted using spectral-dimension information. Finally, the features of various groups are fused, and the checkerboard pattern is relieved by applying the pixel deconvolution layer. As a result, clear and high-resolution images can be produced. The proposed super-resolution algorithm that combines multi-receptive field features with spectral-spatial attention was applied to two public datasets Chikusei and Pavia Center Scene, achieving peak signal-to-noise ratios of 39.869 7 and 31.942 2, respectively and structural similarity of 0.937 6 and 0.878 6, respectively. Therefore, the super-resolution algorithm enjoys obvious performance advantages compared to the latest super-resolution algorithms. Overall, the algorithm proposed in this study integrates the advantages of the multi-receptive field feature extraction module and the spatial-spectral attention module and can significantly improve image details.

Key wordshyperspectral image    image super resolution    multi-receptive field feature extraction    attention mechanism
收稿日期: 2021-04-20      出版日期: 2022-03-14
ZTFLH:  TP751.1  
基金资助:国家自然科学基金面上基金项目“改进BRDF先验知识耦合策略的Landsat30米地表反照率模型研究与验证”编号(42071351);辽宁省教育厅基础研究项目“可见光与红外图像跨域深度行人检测模型研究”编号(LJ2019JL010);辽宁工程技术大学学科创新团队资助项目“智慧农业遥感监测创新团队”共同资助编号(LNTU20TD-23)
通讯作者: 王雅萱
作者简介: 曲海成(1981-),男,博士,副教授,主要研究方向为遥感影像高性能计算、目标检测识别。Email: quhaicheng@lntu.edu.cn
引用本文:   
曲海成, 王雅萱, 申磊. 多感受野特征与空谱注意力结合的高光谱图像超分辨率算法[J]. 自然资源遥感, 2022, 34(1): 43-52.
QU Haicheng, WAND Yaxuan, SHEN Lei. Hyperspectral super-resolution combining multi-receptive field features with spectral-spatial attention. Remote Sensing for Natural Resources, 2022, 34(1): 43-52.
链接本文:  
https://www.gtzyyg.com/CN/10.6046/zrzyyg.2021115      或      https://www.gtzyyg.com/CN/Y2022/V34/I1/43
Fig.1  在Chikusei和Pavia Centre scene数据集上高低频信息
Fig.2  总体网络结构
Fig.3  多感受野特征提取注意力模块
Fig.4  多感受野特征提取模块
Fig.5  空谱结合注意力模块
指标 无MFB MFB MFB+AM
SAM 3.234 8 2.981 9 2.444 6
RMSE 0.014 4 0.013 5 0.012 2
MPSNR 38.298 5 38.832 6 39.869 7
ERGAS 6.290 9 5.897 2 5.191 5
MSSIM 0.911 2 0.921 9 0.937 6
CC 0.932 0 0.939 5 0.951 5
Tab.1  Chikusei数据集在不同模块实验对比
Fig.6  在Chikusei数据集上模块对比
Fig.7  在Pavia Centre scene数据集上模块对比
Fig.8  重叠因子对MPSNR的影响
Fig.9  每组光谱通道数
算法 RMSE MPSNR CC SAM ERGAS MSSIM
Bicubic 0.015 6 37.637 7 0.921 2 3.404 0 6.756 4 0.894 9
VDSR 0.014 8 37.775 5 0.922 7 3.664 2 6.870 8 0.906 5
TLCNN 0.015 0 37.725 1 0.919 6 3.857 3 6.752 2 0.900 8
3DCNN 0.014 0 38.609 1 0.935 5 3.117 4 6.002 6 0.912 7
GDRRN 0.013 7 38.719 8 0.936 9 2.500 0 5.954 0 0.919 3
DeepPrior 0.014 7 38.192 3 0.929 3 3.559 0 6.209 6 0.901 0
本文算法 0.012 2 39.869 7 0.951 5 2.444 6 5.191 5 0.937 6
Tab.2  在Chikusei数据集上不同算法的对比结果
算法 RMSE MPSNR CC SAM ERGAS MSSIM
Bicubic 0.043 7 27.587 4 0.859 4 6.139 9 6.881 4 0.696 1
VDSR 0.041 9 27.882 1 0.865 9 6.700 4 6.699 1 0.724 2
RCAN 0.037 6 28.816 5 0.891 7 5.978 5 6.048 5 0.771 9
TLCNN 0.043 1 27.668 2 0.856 3 6.901 3 6.913 9 0.714 1
3DCNN 0.039 6 28.411 4 0.881 3 5.866 9 6.266 5 0.750 1
DeepPrior 0.041 0 28.106 1 0.872 3 6.266 5 6.484 5 0.736 5
本文算法 0.028 1 31.942 2 0.940 6 6.557 7 6.742 3 0.878 6
Tab.3  在Pavia Centre scene数据集上不同算法的对比结果
算法 RMSE MPSNR CC SAM ERGAS MSSIM
Bicubic 0.021 2 34.721 4 0.986 8 4.175 9 5.271 9 0.927 7
EDSR 0.014 9 38.157 5 0.993 1 3.549 9 3.592 1 0.952 2
RCAN 0.014 2 38.758 5 0.993 5 3.605 0 3.417 8 0.953 0
SAN 0.014 3 38.718 8 0.993 5 3.595 1 3.420 0 0.953 1
3DCNN 0.021 2 34.985 3 0.986 2 4.229 7 7.318 2 0.954 9
GDRRN 0.014 5 38.450 7 0.993 4 3.414 3 3.508 6 0.953 8
本文算法 0.013 9 39.172 9 0.986 5 3.384 2 4.328 4 0.957 2
Tab.4  在CAVE数据集上不同算法的对比结果
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