Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (9): 2963-2969.DOI: 10.11772/j.issn.1001-9081.2022091458

• Multimedia computing and computer simulation • Previous Articles     Next Articles

Remote sensing image pansharpening by convolutional neural network

Kunting LU, Rongrong FEI, Xuande ZHANG()   

  1. School of Electronic Information and Artificial Intelligence,Shaanxi University of Science and Technology,Xi’an Shaanxi 710021,China
  • Received:2022-10-08 Revised:2022-12-01 Accepted:2022-12-06 Online:2023-02-22 Published:2023-09-10
  • Contact: Xuande ZHANG
  • About author:LU Kunting, born in 1999, M. S. candidate. Her research interests include remote sensing image fusion.
    FEI Rongrong, born in 1991, Ph. D., lecturer. Her research interests include remote sensing image processing, computer vision.
  • Supported by:
    National Natural Science Foundation of China(61871260);Natural Science Basic Research Program of Shaanxi Province(2020JQ-346)


路琨婷, 费蓉蓉, 张选德()   

  1. 陕西科技大学 电子信息与人工智能学院,西安 710021
  • 通讯作者: 张选德
  • 作者简介:路琨婷(1999—),女,陕西渭南人,硕士研究生,主要研究方向:遥感图像融合
  • 基金资助:


In the linear injection models of traditional Component Substitution (CS) and Multi-Resolution Analysis (MRA) in remote sensing image pansharpening, the relative spectral response of the sensor used for pansharpening is not considered. At the same time, the insufficient feature extraction of the original images caused by the deep learning-based methods results in the loss of spectral and spatial information in the fusion results. Aiming at the above problems, a pansharpening method combining traditional and deep learning methods was proposed, namely CMRNet. Firstly, CS and MRA were combined with Convolutional Neural Network (CNN) to achieve nonlinearity and improve the performance of pansharpening method. Secondly, the Residual Channel (RC) blocks were designed to realize the fusion and extraction of multi-scale feature information, and the feature maps of different channels were assigned different weights by using Channel Attention (CA) adaptively to learn more effective information. Experiments were conducted on QuickBird and GF1 satellite datasets. Experimental results show that on downscale QuickBird and GF1 datasets, compared with the classic method PanNet, CMRNet has the Peak Signal-to-Noise Ratio (PSNR) increased by 5.48% and 9.62% respectively, and other indexes also improved significantly, which verifies that CMRNet can achieve a better pansharpening effect.

Key words: Convolutional Neural Network (CNN), remote sensing image processing, pansharpening, component substitution, Multi-Resolution Analysis (MRA)



关键词: 卷积神经网络, 遥感图像处理, 全色锐化, 成分替换, 多分辨率分析

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