Super-resolution of satellite imagery using a wavelet multiscale-based deep convolutional neural network model

Nour Aburaed, Alavikunhu Panthakkan, Mina Al-Saad, Marwa El-rai, Saeed Almansoori, Hussain Al Ahmad, Stephen Marshall

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

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Nowadays, satellite images are used in various governmental applications, such as urbanization and monitoring the environment. Spatial resolution is an element of crucial impact on the usage of remote sensing imagery. As such, increasing the spatial resolution of an image is an important pre-processing step that can improve the performance of various image processing tasks, such as segmentation. Once a satellite is launched, the more practical solution to improve the resolution of its captured images is to use Single Image Super Resolution (SISR) techniques. In the recent years, Deep Convolutional Neural Networks (DCNNs) have been recognized as a highly effective tool to reconstruct a High Resolution (HR) image from its Low Resolution (LR) counterpart, which is an open problem due to the inherent difficulty of estimating the missing high frequency components. The aim of this research paper is to design and implement a satellite image SISR algorithm by estimating high frequency details through training Deep Convolutional Neural Network (DCNNs) with respect to wavelet analysis. The goal is to improve the spatial resolution of multispectral remote sensing images captured by DubaiSat-2 satellite. The accuracy of the proposed algorithm is assessed using several metrics such as Peak Signal-to-Noise Ratio (PSNR), Wavelet-based Signal-to-Noise Ratio (WSNR) and Structural Similarity Index Measurement (SSIM).
Original languageEnglish
Title of host publicationProceedings Volume 11533
Subtitle of host publicationImage and Signal Processing for Remote Sensing XXVI
EditorsLorenzo Bruzzone, Francesca Bovolo, Emanuele Santi
Place of PublicationBellingham, WA
Publication statusPublished - 6 Oct 2020


  • satellite images
  • super resolution
  • remote sensing imagery


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