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

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 structu...

Full description

Saved in:
Bibliographic Details
Published in:Journal of sensors 2022-05, Vol.2022, p.1-7
Main Authors: Wang, Yani, Dong, Jinfang, Wang, Bo, Khanna, Shaweta, Singh, Anupam, Hussain, Syed Abid
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Deep learning
Dictionaries
Feature extraction
Image reconstruction
Image resolution
Neural networks
Parameters
Pixels
Propagation
Remote sensing
Research methodology
Satellites
Software
Training
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1687-725X
1687-7268
DOI:10.1155/2022/1777112