Pansharpening based on convolutional autoencoder and multi-scale guided filter

In this paper, we propose a pansharpening method based on a convolutional autoencoder. The convolutional autoencoder is a sort of convolutional neural network (CNN) and objective to scale down the input dimension and typify image features with high exactness. First, the autoencoder network is traine...

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Bibliographic Details
Published in:EURASIP journal on image and video processing Vol. 2021; no. 1; pp. 1 - 20
Main Authors: AL Smadi, Ahmad, Yang, Shuyuan, Kai, Zhang, Mehmood, Atif, Wang, Min, Alsanabani, Ala
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 19.07.2021
Springer Nature B.V
SpringerOpen
Subjects:
Adaptive intensity-hue-saturation AIHS
Artificial neural networks
Biometrics
Convolutional autoencoder
Engineering
Guided image filtering
Image degradation
Image enhancement
Image Processing and Computer Vision
Image reconstruction
Methods
Neural networks
Pansharpening
Pattern Recognition
Remote sensing
Saturation (color)
Semantics
Signal,Image and Speech Processing
Spectra
State-of-the-art reviews
Adaptive intensity-hue-saturation AIHS
Guided image filtering
Convolutional autoencoder
Pansharpening
ISSN:1687-5281, 1687-5176, 1687-5281
Online Access:Get full text
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Summary:In this paper, we propose a pansharpening method based on a convolutional autoencoder. The convolutional autoencoder is a sort of convolutional neural network (CNN) and objective to scale down the input dimension and typify image features with high exactness. First, the autoencoder network is trained to reduce the difference between the degraded panchromatic image patches and reconstruction output original panchromatic image patches. The intensity component, which is developed by adaptive intensity-hue-saturation (AIHS), is then delivered into the trained convolutional autoencoder network to generate an enhanced intensity component of the multi-spectral image. The pansharpening is accomplished by improving the panchromatic image from the enhanced intensity component using a multi-scale guided filter; then, the semantic detail is injected into the upsampled multi-spectral image. Real and degraded datasets are utilized for the experiments, which exhibit that the proposed technique has the ability to preserve the high spatial details and high spectral characteristics simultaneously. Furthermore, experimental results demonstrated that the proposed study performs state-of-the-art results in terms of subjective and objective assessments on remote sensing data.
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ISSN:1687-5281
1687-5176
1687-5281
DOI:10.1186/s13640-021-00565-3