Superresolution Reconstruction Method of Software Remote Sensing Image Based on Convolutional Neural Network

Author:

Wang Yani1ORCID,Dong Jinfang2ORCID,Wang Bo3ORCID,Khanna Shaweta4,Singh Anupam5ORCID,Hussain Syed Abid6ORCID

Affiliation:

1. Xi’an University, Xi’an, Shaanxi 710000, China

2. Shaanxi Meteorological Service Center of Agricultural Remote Sensing and Economic Crops, Xi’an, Shaanxi 710000, China

3. Shaanxi Geomatics Center, Ministry of Natural Resources, Xi’an, Shaanxi 710000, China

4. ITS Engineering College, Greater Noida, U.P., India

5. UPES, Dehradun, India

6. Department of Business Management Bakhtar University, Kabul, Afghanistan

Abstract

In order to solve the problem of long training time for remote sensing image super-resolution reconstruction algorithm, a method for remote sensing image superresolution reconstruction based on convolutional neural network is proposed, which combines dense convolutional network, parallel CNN structure, and subpixel convolution. The features of low-resolution images are extracted using dense convolutional networks, parallel CNNs are used to reduce network parameters, and subpixel convolutions are used to complete feature reconstruction. The results show that the final PSNR value of the black curve with the number of iterations of the three methods in the training process is the highest 27.3, followed by the middle curve, and the worst curve is 27.0. It is proved that the method extracts more features, retains more image details, and improves the reconstruction effect of the image; it greatly reduces the parameters in the network and avoids the phenomenon of overfitting in the deep network.

Funder

Shaanxi Province Natural Science Foundation

Publisher

Hindawi Limited

Subject

Electrical and Electronic Engineering,Instrumentation,Control and Systems Engineering

Reference21 articles.

1. Agricultural climate change based on gis and remote sensing image and the spatial distribution of sports public services;F. He;Arabian Journal of Geosciences,2021

2. Multi-spectral remote sensing images feature coverage classification based on improved convolutional neural network

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