The Analysis about Compressed Sensing Reconstruction Algorithm Based on Machine Learning Applied in Interference Multispectral Images

Interferometric multispectral images contain rich information, so they are widely used in aviation, military, and environmental monitoring. However, the abundant information also leads to the disadvantages that longer time and more physical resources are needed in signal compression and reconstructi...

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Veröffentlicht in:Advances in multimedia Jg. 2021; S. 1 - 6
1. Verfasser: Han, Chang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Hindawi 01.01.2021
John Wiley & Sons, Inc
Wiley
Schlagworte:
Accuracy
Algorithms
Artificial intelligence
Complexity
Environmental monitoring
Image reconstruction
Interference
Machine learning
Military aviation
Optimization algorithms
Spectrum analysis
Wavelet transforms
ISSN:1687-5680, 1687-5699
Online-Zugang:Volltext
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Zusammenfassung:Interferometric multispectral images contain rich information, so they are widely used in aviation, military, and environmental monitoring. However, the abundant information also leads to the disadvantages that longer time and more physical resources are needed in signal compression and reconstruction. In order to make up for the shortcomings of traditional compression and reconstruction algorithms, the stacked convolution denoising autoencoder (SCDA) reconstruction algorithm for interference multispectral images is proposed in this paper. And, the experimental code based on the TensorFlow system is built to reconstruct these images. The results show that, compared with D-AMP and ReconNet algorithms, the SCDA algorithm has the advantages of higher reconstruction accuracy and lower time complexity and space complexity. Therefore, the SCDA algorithm proposed in this paper can be applied to interference multispectral images.
Bibliographie:ObjectType-Article-1
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ISSN:1687-5680
1687-5699
DOI:10.1155/2021/8020473