Convolutional Autoencoder-Based Multispectral Image Fusion

This paper presents a deep learning-based pansharpening method for fusion of panchromatic and multispectral images in remote sensing applications. This method can be categorized as a component substitution method in which a convolutional autoencoder network is trained to generate original panchromat...

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Vydané v:IEEE access Ročník 7; s. 35673 - 35683
Hlavní autori: Azarang, Arian, Manoochehri, Hafez E., Kehtarnavaz, Nasser
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Computer architecture
Computer vision
Convolution
convolutional autoencoder-based pansharpening
Convolutional codes
Deep learning
fusion of panchromatic and multispectral images in remote sensing
High resolution
Image fusion
Image processing
Image reconstruction
Image resolution
Multispectral image fusion by convolutional autoencoder
Remote sensing
Satellite imagery
Spatial resolution
ISSN:2169-3536, 2169-3536
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Shrnutí:This paper presents a deep learning-based pansharpening method for fusion of panchromatic and multispectral images in remote sensing applications. This method can be categorized as a component substitution method in which a convolutional autoencoder network is trained to generate original panchromatic images from their spatially degraded versions. Low resolution multispectral images are then fed into the trained convolutional autoencoder network to generate estimated high resolution multispectral images. The fusion is achieved by injecting the detail map of each spectral band into the corresponding estimated high resolution multispectral bands. Full reference and no-reference metrics are computed for the images of three satellite datasets. These measures are compared with the existing fusion methods whose codes are publicly available. The results obtained indicate the effectiveness of the developed deep learning-based method for multispectral image fusion.
Bibliografia:ObjectType-Article-1
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content type line 14
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2905511