DRformer：一种渐进式耦合多尺度CNN与浓缩注意力Transformer的高光谱图像超分辨率方法

doi:10.11947/j.AGCS.2025.20240485

测绘学报 ›› 2025, Vol. 54 ›› Issue (7): 1230-1242.doi: 10.11947/j.AGCS.2025.20240485

DRformer：一种渐进式耦合多尺度CNN与浓缩注意力Transformer的高光谱图像超分辨率方法

程青(), 汪博轩, 张洪艳()

中国地质大学（武汉）计算机学院，湖北　武汉　430074

收稿日期:2024-12-03 修回日期:2025-07-01 出版日期:2025-08-18 发布日期:2025-08-18
通讯作者: 张洪艳 E-mail:qingcheng@whu.edu.cn;zhanghongyan@cug.edu.cn
作者简介:程青（1987—），女，博士，研究员，博士生导师，主要从事遥感信息处理与应用方面的研究。E-mail：qingcheng@whu.edu.cn
基金资助:
国家重点研发计划(2022YFB3903605);国家自然科学基金(42171383);武汉市自然科学基金(2024040801020278)

DRformer: a progressive coupled multiscale CNN and condensed attention Transformer method for hyperspectral image super-resolution

Qing CHENG(), Boxuan WANG, Hongyan ZHANG()

School of Computer Science, China University of Geosciences, Wuhan 430074, China

Received:2024-12-03 Revised:2025-07-01 Online:2025-08-18 Published:2025-08-18
Contact: Hongyan ZHANG E-mail:qingcheng@whu.edu.cn;zhanghongyan@cug.edu.cn
About author:CHENG Qing (1987—), female, PhD, researcher, PhD supervisor, majors in remote sensing information processing and applications. E-mail: qingcheng@whu.edu.cn
Supported by:
The National Key Research and Development Program of China(2022YFB3903605);The National Natural Science Foundation of China(42171383);Natural Science Foundation of Wuhan(2024040801020278)

摘要/Abstract

摘要：

高光谱图像超分辨率技术旨在通过提升低分辨率高光谱图像的空间细节和质量，使其更好地服务于环境监测等领域。近年来，基于深度卷积神经网络的机器学习技术在光谱单图超分辨率领域上有着广泛的发展与应用，但仍存在难以兼顾空间多尺度局部特征与全局细节特征学习的缺陷。对此，本文设计了一种基于渐进式采样策略耦合卷积神经网络与Transformer架构的融合网络DRformer。一方面，通过多尺度自适应加权光谱关注模块，用于局部特征的多尺度学习并选择性强调光谱信息特征并进行第一次上采样；另一方面，在网络后半段进行第二次上采样后融入基于Transformer架构构建的CADR模块，用于处理图像的全局特征，增强有效信息。为了验证本文方法的有效性与稳健性，选取Chikusei与Houston2013数据集开展试验，相较于已有的GDRRN、SSPSR、EUNet及MSDformer等深度学习方法具有更好的超分辨率性能，并且设计了消融试验以验证本文方法中各模块的有效性。

关键词: 高光谱图像, 超分辨率, Transformer, 注意力机制

Abstract:

The super-resolution technology of hyperspectral image aims to enhance the spatial detail and quality of low-resolution hyperspectral images for better applications in areas such as environmental monitoring. In recent years, machine learning techniques based on deep convolutional neural networks have made significant progress in single hyperspectral image super-resolution. However, challenges remain in balancing the learning of spatial multi-scale local features and global detail features. This paper presents a fusion network, DRformer, that integrates convolutional neural networks and Transformer architecture using a progressive sampling strategy. The network employs a multi-scale adaptive weighted spectral attention module for local feature extraction and selective emphasis of spectral information, followed by an initial upsampling. Subsequently, a CADR module based on the Transformer architecture is incorporated after a second upsampling to process global image features and enhance effective information. To verify the effectiveness and robustness of the network, experiments were conducted on the Chikusei and Houston2013 datasets. The results demonstrate that DRformer outperforms existing deep learning methods, including GDRRN, SSPSR, EUNet and MSDformer in terms of super-resolution performance. Additionally, ablation experiments were carried out to validate the effectiveness of each module in the network.

Key words: hyperspectral image, super-resolution, Transformer, attention mechanism

中图分类号:

P237

程青, 汪博轩, 张洪艳. DRformer：一种渐进式耦合多尺度CNN与浓缩注意力Transformer的高光谱图像超分辨率方法[J]. 测绘学报, 2025, 54(7): 1230-1242.

Qing CHENG, Boxuan WANG, Hongyan ZHANG. DRformer: a progressive coupled multiscale CNN and condensed attention Transformer method for hyperspectral image super-resolution[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(7): 1230-1242.

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参考文献 48

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模块	网络架构	输入尺寸	模型参数	输出尺寸
整体结构	MAWSA	H×W×c	—	H×W×256
	Conv-UP	H×W×256	3×3，c，Stride1	sH/2×sW/2×c
	Concatenation	sH/2×sW/2×c	—	sH/2×sW/2×C
	UP	sH/2×sW/2×C	—	sH×sW×C
	CADR-Conv	sH×sW×C	1×1，256，Stride1	sH×sW×256
	Bicubic-Conv	H×W×C	3×3，256，Stride1	sH×sW×256
	Conv	sH×sW×256	3×3，C，Stride1	sH×sW×C
MAWSA	Dconv（1，3，5）-ReLU	H×W×c	3×3，256，Stride1，dilation（1，3，5）	H×W×256
	Conv-ReLU-Conv	H×W×256	3×3，256，Stride1	H×W×256
	AWCA	H×W×256	reduction=16	H×W×256
CADR	Condensed	sH×sW×C	num＿heads=8，d=16	sH×sW×C
	Attention	sH×sW×C	num＿heads=8，d=16	sH×sW×C
	DRNet	sH×sW×C		sH×sW×C
	（PReLU-Conv）		3×3，256，Stride1
	（Resblock）		3×3，256，Stride1
	（PReLU-Conv）		3×3，C，Stride1

比例因子	方法	PSNR	SSIM	CC	RMSE	ERGAS	SAM
×4	Bicubic	37.637 7	0.895 3	0.921 2	0.015 6	6.756 3	3.403 9
	GDRRN	37.721 8	0.897 0	0.922 3	0.015 4	6.697 0	3.321 5
	SSPSR	39.505 5	0.932 8	0.947 5	0.012 6	5.473 7	2.719 0
	EUNet	38.867 0	0.926 0	0.939 7	0.013 4	5.931 8	2.751 7
	MSDformer	39.067 1	0.924 5	0.942 3	0.013 2	5.714 1	2.864 4
	本文方法	40.085 1	0.933 4	0.943 3	0.012 4	5.224 1	2.697 1
×8	Bicubic	34.504 8	0.806 8	0.831 3	0.022 3	9.697 5	5.043 5
	GDRRN	34.556 4	0.807 3	0.832 7	0.022 2	9.646 9	4.991 5
	SSPSR	34.938 0	0.822 9	0.848 5	0.021 0	9.318 6	4.863 2
	EUNet	35.115 6	0.833 4	0.853 8	0.020 7	9.073 7	4.502 8
	MSDformer	34.790 2	0.816 2	0.842 2	0.021 5	9.433 1	4.823 0
	本文方法	35.913 8	0.822 3	0.810 6	0.020 6	8.594 5	4.478 9

方法	比例因子	PSNR	SSIM	CC	RMSE	ERGAS	SAM
Bicubic	×4	33.698 4	0.801 6	0.901 4	0.025 6	6.157 1	6.459 2
GDRRN	×4	33.798 3	0.806 7	0.903 2	0.025 3	6.084 1	6.342 1
SSPSR	×4	34.207 3	0.831 4	0.910 1	0.023 9	5.748 1	5.494 4
EUNet	×4	34.264 0	0.834 3	0.911 4	0.023 7	5.698 1	5.479 8
MSDformer	×4	34.077 1	0.828 8	0.908 8	0.024 2	5.849 8	5.894 3
本文方法	×4	34.477 7	0.840 1	0.915 5	0.023 1	5.567 0	5.355 8
Bicubic	×8	31.120 9	0.673 0	0.816 3	0.034 7	8.339 1	9.365 7
GDRRN	×8	31.097 6	0.674 2	0.814 0	0.034 5	8.320 1	9.368 8
SSPSR	×8	31.221 5	0.684 1	0.820 7	0.033 9	8.174 1	8.895 9
EUNet	×8	31.349 1	0.687 7	0.824 1	0.033 5	8.055 6	8.634 9
MSDformer	×8	31.305 1	0.688 2	0.823 0	0.033 6	8.108 1	9.079 3
本文方法	×8	31.350 2	0.690 4	0.824 2	0.033 5	8.120 1	8.787 1

模型	参数量/MB	时间/s
GDRRN	0.479 2	304
SSPSR	13.674 8	1828
EUNet	2.796 7	867
MSDformer	9.121 5	1535
DRformer	12.988 2	4101

变体	参数量/MB	PSNR	SSIM	CC	RMSE	ERGAS	SAM
本文方法	12.988 2	34.477 7	0.840 1	0.915 5	0.023 1	5.567 0	5.355 8
变体1	14.241 6	34.229 5	0.830 7	0.910 9	0.023 7	5.728 5	5.654 3
变体2	12.988 2	33.968 1	0.827 6	0.907 1	0.024 1	5.883 1	6.114 6
变体3	9.095 6	34.181 7	0.828 9	0.910 4	0.023 9	5.760 4	5.558 5
变体4	5.689 8	34.112 1	0.827 5	0.909 2	0.024 1	5.818 8	5.985 3

DRformer：一种渐进式耦合多尺度CNN与浓缩注意力Transformer的高光谱图像超分辨率方法

DRformer: a progressive coupled multiscale CNN and condensed attention Transformer method for hyperspectral image super-resolution

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图/表 14

参考文献 48

相关文章 15

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Metrics

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CADR块数	PSNR	SSIM	CC	RMSE	ERGAS	SAM
N=1	34.116 3	0.828 1	0.909 2	0.024 1	6.157 1	6.023 3
N=2	34.477 7	0.840 1	0.915 5	0.023 1	5.567 0	5.355 8
N=3	34.456 1	0.838 6	0.915 4	0.023 3	5.570 8	5.455 4

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