面向真实场景的高分辨率遥感影像超分辨率重建

doi:10.7523/j.ucas.2024.054

摘要/Abstract

摘要：

超分辨率技术以其灵活、低成本等特点已经成为构建高分辨率数据集以及补充高分辨率影像不足的重要手段。相较于自然图像，反映真实场景的遥感影像由于其自身的复杂性与特殊性加大了超分辨率任务的难度。同时对于遥感影像，传统的深度学习模型虽然在一定程度上可以提高分辨率，但是对于地物细节和纹理的提升还存在着很大不足。因此，本文在对抗生成网络模型的基础上，融合通道-空间注意力来增强网络的特征学习能力，并使用伪影抑制策略来区分平滑区域和细节丰富区域，让网络专注于细节丰富区域，抑制伪影的产生。在高分卫星数据上的大量实验表明，本文方法的定量指标和视觉质量优于目前主流的方法。

关键词: 超分辨率, 遥感影像, 对抗生成网络, 融合通道-空间注意力, 伪影抑制

Abstract:

Super-resolution technology has become an important tool for reconstructing high-resolution datasets and supplementing the shortage of high-resolution images with its characteristics of flexibility and low cost. Compared with natural images， remote sensing images of real scenes are complex and specific， which make super-resolution tasks more difficult. Meanwhile， for remote sensing images， traditional deep learning models can improve the resolution， but there is still a great deficiency of improvement for the details and textures of the ground objects. Therefore， based on the generative adversarial network model， this paper fuses channel-space attention to enhance the feature learning capability of the network and use an artifact suppression strategy to distinguish smooth regions from detail-rich regions， so that the network can focus more on detail-rich regions and suppress the generation of artifacts. Extensive experiments on GaoFen satellite data show that the quantitative metrics and visual quality of the method proposed in this paper are better than those of the current mainstream methods.

Key words: super-resolution, remote sensing images, GAN, fusing channel-space attention, artifact suppression

中图分类号:

TP751

赵佳祎, 马勇, 陈甫, 姚武韬, 尚二萍, 仉淑艳, 龙安. 面向真实场景的高分辨率遥感影像超分辨率重建[J]. 中国科学院大学学报, 2026, 43(1): 80-92.

Jiayi ZHAO, Yong MA, Fu CHEN, Wutao YAO, Erping SHANG, Shuyan ZHANG, An LONG. Super-resolution reconstruction of high-resolution remote sensing images for real scenes[J]. Journal of University of Chinese Academy of Sciences, 2026, 43(1): 80-92.

图/表 13

图1 总体流程图

Fig.1 Overall flow chart

表1 数据集中卫星及空间分辨率 (m)

Table 1 Satellite and spatial resolution ofthe dataset

卫星	全色波段分辨率	多光谱波段分辨率
GF1	2.0	8.0
GF1B/C/D	2.0	8.0
GF2	0.8（星下点）	3.2（星下点）
GF6	1.0	2.0
GF7	0.8	3.2

图2 网络框架图

Fig.2 Network framework diagram

图3 CSA的内部结构

Fig.3 Internal structure of CSA

图4 HR影像和LR图像超分后图像对比图

Fig.4 Comparison of the HR image with the real LR image

表2 模型的详细配置

Table 2 Detailed configuration of the model

参数	设置
批次规格	8
训练迭代次数	450 000
优化方法	Adam, $β 1 = 0.9$ , $β 2 = 0.99$
学习率	1e-4

表2 模型的详细配置

Table 2 Detailed configuration of the model

参数	设置
批次规格	8
训练迭代次数	450 000
优化方法	Adam, $β 1 = 0.9$ , $β 2 = 0.99$
学习率	1e-4

图5 本文算法与真实HR影像对比

Fig. 5 Comparison of the algorithm in this paper with the real HR image

表3 不同模型在数据集上的指标对比

Table 3 Comparison of metrics of different models on the data set

模型	PSNR	SSIM	FID	LPIPS
Bicubic	16.894 1	0.574 8	0.545 4	71.680 0
SRResNet	26.326 5	0.662 6	0.423 0	66.757 3
EDSR	27.417 6	0.717 2	0.352 0	51.435 7
RCAN	26.685 1	0.682 8	0.399 5	54.029 1
SRGAN	24.772 9	0.592 2	0.289 2	20.051 4
ESRGAN	24.822 0	0.590 6	0.282 9	19.126 6
本文	25.741 8	0.647 4	0.246 5	18.247 6

图6 不同模型在不同场景下恢复结果的对比

Fig.6 Comparison of recovery results of different models in different scenarios

表4 消融实验中指标的比较

Table 4 Comparison of indicators in ablation experiments

添加的模块	PSNR	SSIM	FID	LPIPS
无	24.892 7	0.614 6	0.274 1	18.947 2
伪影抑制	25.472 8	0.632 1	0.254 2	18.627 5
融合注意力	25.501 7	0.638 6	0.253 3	18.872 1
伪影抑制+融合注意力	25.741 8	0.647 4	0.246 5	18.247 6

图7 消融实验中恢复结果对比

Fig.7 Comparison of recovery results in ablation experiments

表5 不同模型在不同区域的2倍超分辨率重建结果的定量评价

Table 5 Quantitative evaluation of 2x super-resolution reconstruction results of different models in different regions

区域	SRResNet	EDSR	RCAN	SRGAN	ESRGAN	本文
陕西汉中	8.215 6	8.157 9	8.519 6	6.254 9	6.545 5	6.182 7
北京	6.683 7	8.013 9	8.110 7	4.769 0	5.292 6	4.743 9

图8 在不同时间和区域上的迁移实验结果

Fig.8 Results of the migration experiment at different times and regions

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