MDECNN: A Multiscale Perception Dense Encoding Convolutional Neural Network for Multispectral Pan-Sharpening

With the rapid development of deep neural networks in the field of remote sensing image fusion, the pan-sharpening method based on convolutional neural networks has achieved remarkable effects. However, because remote sensing images contain complex features, existing methods cannot fully extract spa...

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Published in:	Remote sensing (Basel, Switzerland) Vol. 13; no. 3; p. 535
Main Authors:	Li, Weisheng, Liang, Xuesong, Dong, Meilin
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.02.2021
Subjects:	Algorithms Artificial intelligence Artificial neural networks Coding comprehensive loss Computer vision convolutional neural network data collection Decomposition Deep learning dense coding feature enhancement Feature extraction Image enhancement Image processing Image quality Image reconstruction Information processing Mapping Methods multiscale perception Neural coding Neural networks Perception Principal components analysis Remote sensing Satellite imagery Satellites Sensors Sharpening Spatial data Spectra Wavelet transforms
ISSN:	2072-4292, 2072-4292
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Abstract	With the rapid development of deep neural networks in the field of remote sensing image fusion, the pan-sharpening method based on convolutional neural networks has achieved remarkable effects. However, because remote sensing images contain complex features, existing methods cannot fully extract spatial features while maintaining spectral quality, resulting in insufficient reconstruction capabilities. To produce high-quality pan-sharpened images, a multiscale perception dense coding convolutional neural network (MDECNN) is proposed. The network is based on dual-stream input, designing multiscale blocks to separately extract the rich spatial information contained in panchromatic (PAN) images, designing feature enhancement blocks and dense coding structures to fully learn the feature mapping relationship, and proposing comprehensive loss constraint expectations. Spectral mapping is used to maintain spectral quality and obtain high-quality fused images. Experiments on different satellite datasets show that this method is superior to the existing methods in both subjective and objective evaluations.
AbstractList	With the rapid development of deep neural networks in the field of remote sensing image fusion, the pan-sharpening method based on convolutional neural networks has achieved remarkable effects. However, because remote sensing images contain complex features, existing methods cannot fully extract spatial features while maintaining spectral quality, resulting in insufficient reconstruction capabilities. To produce high-quality pan-sharpened images, a multiscale perception dense coding convolutional neural network (MDECNN) is proposed. The network is based on dual-stream input, designing multiscale blocks to separately extract the rich spatial information contained in panchromatic (PAN) images, designing feature enhancement blocks and dense coding structures to fully learn the feature mapping relationship, and proposing comprehensive loss constraint expectations. Spectral mapping is used to maintain spectral quality and obtain high-quality fused images. Experiments on different satellite datasets show that this method is superior to the existing methods in both subjective and objective evaluations.
Author	Dong, Meilin Li, Weisheng Liang, Xuesong
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SubjectTerms	Algorithms Artificial intelligence Artificial neural networks Coding comprehensive loss Computer vision convolutional neural network data collection Decomposition Deep learning dense coding feature enhancement Feature extraction Image enhancement Image processing Image quality Image reconstruction Information processing Mapping Methods multiscale perception Neural coding Neural networks Perception Principal components analysis Remote sensing Satellite imagery Satellites Sensors Sharpening Spatial data Spectra Wavelet transforms
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Title	MDECNN: A Multiscale Perception Dense Encoding Convolutional Neural Network for Multispectral Pan-Sharpening
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