Abstract
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.
Highlights
Interference spectroscopy technology began in the 1980s, which integrates spectroscopy, precision mechanics, electronics, and other disciplines [1, 2]
Taking the interferometric multispectral images as the research object, at first, this paper studies the theory and research results of reconstruction algorithm related to compressed sensing
We can find the following: (1) When the measurement rate is in the range of 0.01–0.20, the number of network parameters based on ReconNet algorithm and stacked convolution denoising autoencoder (SCDA) algorithm is positively correlated with the measurement rate
Summary
Laboratory of Control and Application Technology of Robotic, Wuhan Business University, Wuhan, Hubei 430056, China. Interferometric multispectral images contain rich information, so they are widely used in aviation, military, and environmental monitoring. The abundant information 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. The experimental code based on the TensorFlow system is built to reconstruct these images. E 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. Erefore, the SCDA algorithm proposed in this paper can be applied to interference multispectral images The experimental code based on the TensorFlow system is built to reconstruct these images. e 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. erefore, the SCDA algorithm proposed in this paper can be applied to interference multispectral images
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