Efficient channel attention algorithm for super-resolution reconstruction of remote sensing image

CHEN Xiaoxuan; TONG Xiaodan; LI Yaowei; CAI Yuhan; JIANG Bo

doi:10.16152/j.cnki.xdxbzr.2026-01-010

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Efficient channel attention algorithm for super-resolution reconstruction of remote sensing image

更新时间：2026-02-24

- Efficient channel attention algorithm for super-resolution reconstruction of remote sensing image
- Journal of Northwest University (Natural Science Edition) Vol. 56, Issue 1, Pages: 108-117(2026)
- 作者机构：
  
  1.西北大学　电子信息学院（人工智能学院），陕西　西安　710127
  2.生成式人工智能与混合现实陕西省高等学校重点实验室，陕西　西安　710127
- 作者简介：
- 基金信息：
- DOI：10.16152/j.cnki.xdxbzr.2026-01-010
  CLC： TP391
- Received：11 July 2025，
  
  Revised：2025-09-08，
  
  Published：25 February 2026
- 稿件说明：
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CHEN Xiaoxuan, TONG Xiaodan, LI Yaowei, et al. Efficient channel attention algorithm for super-resolution reconstruction of remote sensing image[J]. Journal of Northwest University (Natural Science Edition), 2026, 56(1): 108-117.
DOI：

CHEN Xiaoxuan, TONG Xiaodan, LI Yaowei, et al. Efficient channel attention algorithm for super-resolution reconstruction of remote sensing image[J]. Journal of Northwest University (Natural Science Edition), 2026, 56(1): 108-117. DOI： 10.16152/j.cnki.xdxbzr.2026-01-010.

摘要

遥感成像设备普遍面临距离远、成像分辨率低的问题，直接影响遥感图像质量和应用效果。针对这一问题，提出一种基于高效通道注意力的特征增强超分辨率重建网络模型，将图像超分辨率重建技术引入到遥感图像处理领域，利用分组卷积特征增强模块对图像进行特征提取和增强，然后利用高效通道注意力和非对称卷积并联构成的注意力模块，建立起图像不同区域之间的相互关系，重建出高分辨率图像。实验结果表明，该算法在WHU-RS19测试集上的峰值信噪比和结构相似性分别为28.70 dB、0.753 9，分别比次优方法提高了0.19 dB和0.006 6，重建图像的细节也更加丰富，从客观指标和主观视觉上都验证了该算法的有效性。

Abstract

Remote sensing imaging equipment generally faces the problems of long distance and low imaging resolution

which directly affects the quality and application effect of remote sensing images. In order to solve this problem

a super-resolution reconstruction network based on feature enhancement of efficient channel attention is proposed. It introduces image super-resolution reconstruction technology into the field of remote sensing image processing

uses the grouped convolution feature enhancement module to extract and enhance the features of the image

and then uses the attention module composed of efficient channel attention and asymmetric convolution in parallel to establish the relationship between different regions of the image

and reconstructs low-resolution remote sensing images to obtain idealized high-resolution images. Finally

the experimental results show that the peak signal-to-noise ratio and structural similarity of this algorithm on the WHU-RS19 test set are 28.70 dB and 0.753 9

which are 0.19 dB and 0.006 6 higher than those of the suboptimal method

respectively

and the details of the reconstructed image are more abundant.

关键词

Keywords

references

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