Super-Resolution Reconstruction of Remote Sensing Images Using Generative Adversarial Network With Shallow Information Enhancement

The super-resolution (SR) reconstruction method based on deep learning can significantly improve the spatial SR of remote sensing images. However, the current methods make insufficient use of the remote context information and channel information in shallow feature extraction, resulting in the limit...
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Autor*in:	Yujia Fu [verfasserIn] Xiangrong Zhang [verfasserIn] Mingyang Wang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Generative adversarial network (GAN) multiscale shallow information remote sensing images super-resolution (SR) reconstruction

Übergeordnetes Werk:	In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing - IEEE, 2020, 15(2022), Seite 8529-8540
Übergeordnetes Werk:	volume:15 ; year:2022 ; pages:8529-8540

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/JSTARS.2022.3209819

Katalog-ID:	DOAJ081674368

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520			\|a The super-resolution (SR) reconstruction method based on deep learning can significantly improve the spatial SR of remote sensing images. However, the current methods make insufficient use of the remote context information and channel information in shallow feature extraction, resulting in the limited effect of SR reconstruction. This article proposed a new SR reconstruction model, SIEGAN, which uses generative adversarial network with shallow information enhancement to improve the effect of SR reconstruction of remote sensing images. Similar to other generative adversarial models, SIEGAN is composed of generator and discriminator. But SIEGAN enhances the generator's ability to extract shallow information by using three different scale convolution operations. Specifically, a depthwise convolution is used to extract the local context information of each band of the image. A depthwise dilation convolution is used to capture the remote context information in the image. Finally, a 1×1 convolution is used to extract the correlation features between different channels in remote sensing images. In addition, SIEGAN uses U-Net network as its discriminator to provide detailed feedback per pixel to the generator to improve the model's ability to identify image details. And the spectral–spatial total variation loss function is introduced to ensure the spectral–spatial reliability of the reconstructed images. The experimental results on Gaofen-1 data proved that compared with the state-of-the-art models, SIEGAN has achieved better SR reconstruction performance. Furthermore, the reconstructed images by SIEGAN demonstrate better performance in land cover classification.
650		4	\|a Generative adversarial network (GAN)
650		4	\|a multiscale shallow information
650		4	\|a remote sensing images
650		4	\|a super-resolution (SR) reconstruction
653		0	\|a Ocean engineering
653		0	\|a Geophysics. Cosmic physics
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700	0		\|a Mingyang Wang \|e verfasserin \|4 aut
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allfields	10.1109/JSTARS.2022.3209819 doi (DE-627)DOAJ081674368 (DE-599)DOAJ6bfd53d2ba154b6ea2d668b68af67f74 DE-627 ger DE-627 rakwb eng TC1501-1800 QC801-809 Yujia Fu verfasserin aut Super-Resolution Reconstruction of Remote Sensing Images Using Generative Adversarial Network With Shallow Information Enhancement 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The super-resolution (SR) reconstruction method based on deep learning can significantly improve the spatial SR of remote sensing images. However, the current methods make insufficient use of the remote context information and channel information in shallow feature extraction, resulting in the limited effect of SR reconstruction. This article proposed a new SR reconstruction model, SIEGAN, which uses generative adversarial network with shallow information enhancement to improve the effect of SR reconstruction of remote sensing images. Similar to other generative adversarial models, SIEGAN is composed of generator and discriminator. But SIEGAN enhances the generator's ability to extract shallow information by using three different scale convolution operations. Specifically, a depthwise convolution is used to extract the local context information of each band of the image. A depthwise dilation convolution is used to capture the remote context information in the image. Finally, a 1×1 convolution is used to extract the correlation features between different channels in remote sensing images. In addition, SIEGAN uses U-Net network as its discriminator to provide detailed feedback per pixel to the generator to improve the model's ability to identify image details. And the spectral–spatial total variation loss function is introduced to ensure the spectral–spatial reliability of the reconstructed images. The experimental results on Gaofen-1 data proved that compared with the state-of-the-art models, SIEGAN has achieved better SR reconstruction performance. Furthermore, the reconstructed images by SIEGAN demonstrate better performance in land cover classification. Generative adversarial network (GAN) multiscale shallow information remote sensing images super-resolution (SR) reconstruction Ocean engineering Geophysics. Cosmic physics Xiangrong Zhang verfasserin aut Mingyang Wang verfasserin aut In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE, 2020 15(2022), Seite 8529-8540 (DE-627)581732634 (DE-600)2457423-5 21511535 nnns volume:15 year:2022 pages:8529-8540 https://doi.org/10.1109/JSTARS.2022.3209819 kostenfrei https://doaj.org/article/6bfd53d2ba154b6ea2d668b68af67f74 kostenfrei https://ieeexplore.ieee.org/document/9903573/ kostenfrei https://doaj.org/toc/2151-1535 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_2522 GBV_ILN_2965 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2022 8529-8540
spelling	10.1109/JSTARS.2022.3209819 doi (DE-627)DOAJ081674368 (DE-599)DOAJ6bfd53d2ba154b6ea2d668b68af67f74 DE-627 ger DE-627 rakwb eng TC1501-1800 QC801-809 Yujia Fu verfasserin aut Super-Resolution Reconstruction of Remote Sensing Images Using Generative Adversarial Network With Shallow Information Enhancement 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The super-resolution (SR) reconstruction method based on deep learning can significantly improve the spatial SR of remote sensing images. However, the current methods make insufficient use of the remote context information and channel information in shallow feature extraction, resulting in the limited effect of SR reconstruction. This article proposed a new SR reconstruction model, SIEGAN, which uses generative adversarial network with shallow information enhancement to improve the effect of SR reconstruction of remote sensing images. Similar to other generative adversarial models, SIEGAN is composed of generator and discriminator. But SIEGAN enhances the generator's ability to extract shallow information by using three different scale convolution operations. Specifically, a depthwise convolution is used to extract the local context information of each band of the image. A depthwise dilation convolution is used to capture the remote context information in the image. Finally, a 1×1 convolution is used to extract the correlation features between different channels in remote sensing images. In addition, SIEGAN uses U-Net network as its discriminator to provide detailed feedback per pixel to the generator to improve the model's ability to identify image details. And the spectral–spatial total variation loss function is introduced to ensure the spectral–spatial reliability of the reconstructed images. The experimental results on Gaofen-1 data proved that compared with the state-of-the-art models, SIEGAN has achieved better SR reconstruction performance. Furthermore, the reconstructed images by SIEGAN demonstrate better performance in land cover classification. Generative adversarial network (GAN) multiscale shallow information remote sensing images super-resolution (SR) reconstruction Ocean engineering Geophysics. Cosmic physics Xiangrong Zhang verfasserin aut Mingyang Wang verfasserin aut In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE, 2020 15(2022), Seite 8529-8540 (DE-627)581732634 (DE-600)2457423-5 21511535 nnns volume:15 year:2022 pages:8529-8540 https://doi.org/10.1109/JSTARS.2022.3209819 kostenfrei https://doaj.org/article/6bfd53d2ba154b6ea2d668b68af67f74 kostenfrei https://ieeexplore.ieee.org/document/9903573/ kostenfrei https://doaj.org/toc/2151-1535 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_2522 GBV_ILN_2965 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2022 8529-8540
allfields_unstemmed	10.1109/JSTARS.2022.3209819 doi (DE-627)DOAJ081674368 (DE-599)DOAJ6bfd53d2ba154b6ea2d668b68af67f74 DE-627 ger DE-627 rakwb eng TC1501-1800 QC801-809 Yujia Fu verfasserin aut Super-Resolution Reconstruction of Remote Sensing Images Using Generative Adversarial Network With Shallow Information Enhancement 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The super-resolution (SR) reconstruction method based on deep learning can significantly improve the spatial SR of remote sensing images. However, the current methods make insufficient use of the remote context information and channel information in shallow feature extraction, resulting in the limited effect of SR reconstruction. This article proposed a new SR reconstruction model, SIEGAN, which uses generative adversarial network with shallow information enhancement to improve the effect of SR reconstruction of remote sensing images. Similar to other generative adversarial models, SIEGAN is composed of generator and discriminator. But SIEGAN enhances the generator's ability to extract shallow information by using three different scale convolution operations. Specifically, a depthwise convolution is used to extract the local context information of each band of the image. A depthwise dilation convolution is used to capture the remote context information in the image. Finally, a 1×1 convolution is used to extract the correlation features between different channels in remote sensing images. In addition, SIEGAN uses U-Net network as its discriminator to provide detailed feedback per pixel to the generator to improve the model's ability to identify image details. And the spectral–spatial total variation loss function is introduced to ensure the spectral–spatial reliability of the reconstructed images. The experimental results on Gaofen-1 data proved that compared with the state-of-the-art models, SIEGAN has achieved better SR reconstruction performance. Furthermore, the reconstructed images by SIEGAN demonstrate better performance in land cover classification. Generative adversarial network (GAN) multiscale shallow information remote sensing images super-resolution (SR) reconstruction Ocean engineering Geophysics. Cosmic physics Xiangrong Zhang verfasserin aut Mingyang Wang verfasserin aut In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE, 2020 15(2022), Seite 8529-8540 (DE-627)581732634 (DE-600)2457423-5 21511535 nnns volume:15 year:2022 pages:8529-8540 https://doi.org/10.1109/JSTARS.2022.3209819 kostenfrei https://doaj.org/article/6bfd53d2ba154b6ea2d668b68af67f74 kostenfrei https://ieeexplore.ieee.org/document/9903573/ kostenfrei https://doaj.org/toc/2151-1535 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_2522 GBV_ILN_2965 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2022 8529-8540
allfieldsGer	10.1109/JSTARS.2022.3209819 doi (DE-627)DOAJ081674368 (DE-599)DOAJ6bfd53d2ba154b6ea2d668b68af67f74 DE-627 ger DE-627 rakwb eng TC1501-1800 QC801-809 Yujia Fu verfasserin aut Super-Resolution Reconstruction of Remote Sensing Images Using Generative Adversarial Network With Shallow Information Enhancement 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The super-resolution (SR) reconstruction method based on deep learning can significantly improve the spatial SR of remote sensing images. However, the current methods make insufficient use of the remote context information and channel information in shallow feature extraction, resulting in the limited effect of SR reconstruction. This article proposed a new SR reconstruction model, SIEGAN, which uses generative adversarial network with shallow information enhancement to improve the effect of SR reconstruction of remote sensing images. Similar to other generative adversarial models, SIEGAN is composed of generator and discriminator. But SIEGAN enhances the generator's ability to extract shallow information by using three different scale convolution operations. Specifically, a depthwise convolution is used to extract the local context information of each band of the image. A depthwise dilation convolution is used to capture the remote context information in the image. Finally, a 1×1 convolution is used to extract the correlation features between different channels in remote sensing images. In addition, SIEGAN uses U-Net network as its discriminator to provide detailed feedback per pixel to the generator to improve the model's ability to identify image details. And the spectral–spatial total variation loss function is introduced to ensure the spectral–spatial reliability of the reconstructed images. The experimental results on Gaofen-1 data proved that compared with the state-of-the-art models, SIEGAN has achieved better SR reconstruction performance. Furthermore, the reconstructed images by SIEGAN demonstrate better performance in land cover classification. Generative adversarial network (GAN) multiscale shallow information remote sensing images super-resolution (SR) reconstruction Ocean engineering Geophysics. Cosmic physics Xiangrong Zhang verfasserin aut Mingyang Wang verfasserin aut In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE, 2020 15(2022), Seite 8529-8540 (DE-627)581732634 (DE-600)2457423-5 21511535 nnns volume:15 year:2022 pages:8529-8540 https://doi.org/10.1109/JSTARS.2022.3209819 kostenfrei https://doaj.org/article/6bfd53d2ba154b6ea2d668b68af67f74 kostenfrei https://ieeexplore.ieee.org/document/9903573/ kostenfrei https://doaj.org/toc/2151-1535 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_2522 GBV_ILN_2965 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2022 8529-8540
allfieldsSound	10.1109/JSTARS.2022.3209819 doi (DE-627)DOAJ081674368 (DE-599)DOAJ6bfd53d2ba154b6ea2d668b68af67f74 DE-627 ger DE-627 rakwb eng TC1501-1800 QC801-809 Yujia Fu verfasserin aut Super-Resolution Reconstruction of Remote Sensing Images Using Generative Adversarial Network With Shallow Information Enhancement 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The super-resolution (SR) reconstruction method based on deep learning can significantly improve the spatial SR of remote sensing images. However, the current methods make insufficient use of the remote context information and channel information in shallow feature extraction, resulting in the limited effect of SR reconstruction. This article proposed a new SR reconstruction model, SIEGAN, which uses generative adversarial network with shallow information enhancement to improve the effect of SR reconstruction of remote sensing images. Similar to other generative adversarial models, SIEGAN is composed of generator and discriminator. But SIEGAN enhances the generator's ability to extract shallow information by using three different scale convolution operations. Specifically, a depthwise convolution is used to extract the local context information of each band of the image. A depthwise dilation convolution is used to capture the remote context information in the image. Finally, a 1×1 convolution is used to extract the correlation features between different channels in remote sensing images. In addition, SIEGAN uses U-Net network as its discriminator to provide detailed feedback per pixel to the generator to improve the model's ability to identify image details. And the spectral–spatial total variation loss function is introduced to ensure the spectral–spatial reliability of the reconstructed images. The experimental results on Gaofen-1 data proved that compared with the state-of-the-art models, SIEGAN has achieved better SR reconstruction performance. Furthermore, the reconstructed images by SIEGAN demonstrate better performance in land cover classification. Generative adversarial network (GAN) multiscale shallow information remote sensing images super-resolution (SR) reconstruction Ocean engineering Geophysics. Cosmic physics Xiangrong Zhang verfasserin aut Mingyang Wang verfasserin aut In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE, 2020 15(2022), Seite 8529-8540 (DE-627)581732634 (DE-600)2457423-5 21511535 nnns volume:15 year:2022 pages:8529-8540 https://doi.org/10.1109/JSTARS.2022.3209819 kostenfrei https://doaj.org/article/6bfd53d2ba154b6ea2d668b68af67f74 kostenfrei https://ieeexplore.ieee.org/document/9903573/ kostenfrei https://doaj.org/toc/2151-1535 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_2522 GBV_ILN_2965 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2022 8529-8540
language	English
source	In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 15(2022), Seite 8529-8540 volume:15 year:2022 pages:8529-8540
sourceStr	In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 15(2022), Seite 8529-8540 volume:15 year:2022 pages:8529-8540
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Generative adversarial network (GAN) multiscale shallow information remote sensing images super-resolution (SR) reconstruction Ocean engineering Geophysics. Cosmic physics
isfreeaccess_bool	true
container_title	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
authorswithroles_txt_mv	Yujia Fu @@aut@@ Xiangrong Zhang @@aut@@ Mingyang Wang @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	581732634
id	DOAJ081674368
language_de	englisch
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callnumber-first	T - Technology
author	Yujia Fu
spellingShingle	Yujia Fu misc TC1501-1800 misc QC801-809 misc Generative adversarial network (GAN) misc multiscale shallow information misc remote sensing images misc super-resolution (SR) reconstruction misc Ocean engineering misc Geophysics. Cosmic physics Super-Resolution Reconstruction of Remote Sensing Images Using Generative Adversarial Network With Shallow Information Enhancement
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topic_title	TC1501-1800 QC801-809 Super-Resolution Reconstruction of Remote Sensing Images Using Generative Adversarial Network With Shallow Information Enhancement Generative adversarial network (GAN) multiscale shallow information remote sensing images super-resolution (SR) reconstruction
topic	misc TC1501-1800 misc QC801-809 misc Generative adversarial network (GAN) misc multiscale shallow information misc remote sensing images misc super-resolution (SR) reconstruction misc Ocean engineering misc Geophysics. Cosmic physics
topic_unstemmed	misc TC1501-1800 misc QC801-809 misc Generative adversarial network (GAN) misc multiscale shallow information misc remote sensing images misc super-resolution (SR) reconstruction misc Ocean engineering misc Geophysics. Cosmic physics
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title	Super-Resolution Reconstruction of Remote Sensing Images Using Generative Adversarial Network With Shallow Information Enhancement
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title_full	Super-Resolution Reconstruction of Remote Sensing Images Using Generative Adversarial Network With Shallow Information Enhancement
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title_sort	super-resolution reconstruction of remote sensing images using generative adversarial network with shallow information enhancement
callnumber	TC1501-1800
title_auth	Super-Resolution Reconstruction of Remote Sensing Images Using Generative Adversarial Network With Shallow Information Enhancement
abstract	The super-resolution (SR) reconstruction method based on deep learning can significantly improve the spatial SR of remote sensing images. However, the current methods make insufficient use of the remote context information and channel information in shallow feature extraction, resulting in the limited effect of SR reconstruction. This article proposed a new SR reconstruction model, SIEGAN, which uses generative adversarial network with shallow information enhancement to improve the effect of SR reconstruction of remote sensing images. Similar to other generative adversarial models, SIEGAN is composed of generator and discriminator. But SIEGAN enhances the generator's ability to extract shallow information by using three different scale convolution operations. Specifically, a depthwise convolution is used to extract the local context information of each band of the image. A depthwise dilation convolution is used to capture the remote context information in the image. Finally, a 1×1 convolution is used to extract the correlation features between different channels in remote sensing images. In addition, SIEGAN uses U-Net network as its discriminator to provide detailed feedback per pixel to the generator to improve the model's ability to identify image details. And the spectral–spatial total variation loss function is introduced to ensure the spectral–spatial reliability of the reconstructed images. The experimental results on Gaofen-1 data proved that compared with the state-of-the-art models, SIEGAN has achieved better SR reconstruction performance. Furthermore, the reconstructed images by SIEGAN demonstrate better performance in land cover classification.
abstractGer	The super-resolution (SR) reconstruction method based on deep learning can significantly improve the spatial SR of remote sensing images. However, the current methods make insufficient use of the remote context information and channel information in shallow feature extraction, resulting in the limited effect of SR reconstruction. This article proposed a new SR reconstruction model, SIEGAN, which uses generative adversarial network with shallow information enhancement to improve the effect of SR reconstruction of remote sensing images. Similar to other generative adversarial models, SIEGAN is composed of generator and discriminator. But SIEGAN enhances the generator's ability to extract shallow information by using three different scale convolution operations. Specifically, a depthwise convolution is used to extract the local context information of each band of the image. A depthwise dilation convolution is used to capture the remote context information in the image. Finally, a 1×1 convolution is used to extract the correlation features between different channels in remote sensing images. In addition, SIEGAN uses U-Net network as its discriminator to provide detailed feedback per pixel to the generator to improve the model's ability to identify image details. And the spectral–spatial total variation loss function is introduced to ensure the spectral–spatial reliability of the reconstructed images. The experimental results on Gaofen-1 data proved that compared with the state-of-the-art models, SIEGAN has achieved better SR reconstruction performance. Furthermore, the reconstructed images by SIEGAN demonstrate better performance in land cover classification.
abstract_unstemmed	The super-resolution (SR) reconstruction method based on deep learning can significantly improve the spatial SR of remote sensing images. However, the current methods make insufficient use of the remote context information and channel information in shallow feature extraction, resulting in the limited effect of SR reconstruction. This article proposed a new SR reconstruction model, SIEGAN, which uses generative adversarial network with shallow information enhancement to improve the effect of SR reconstruction of remote sensing images. Similar to other generative adversarial models, SIEGAN is composed of generator and discriminator. But SIEGAN enhances the generator's ability to extract shallow information by using three different scale convolution operations. Specifically, a depthwise convolution is used to extract the local context information of each band of the image. A depthwise dilation convolution is used to capture the remote context information in the image. Finally, a 1×1 convolution is used to extract the correlation features between different channels in remote sensing images. In addition, SIEGAN uses U-Net network as its discriminator to provide detailed feedback per pixel to the generator to improve the model's ability to identify image details. And the spectral–spatial total variation loss function is introduced to ensure the spectral–spatial reliability of the reconstructed images. The experimental results on Gaofen-1 data proved that compared with the state-of-the-art models, SIEGAN has achieved better SR reconstruction performance. Furthermore, the reconstructed images by SIEGAN demonstrate better performance in land cover classification.
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title_short	Super-Resolution Reconstruction of Remote Sensing Images Using Generative Adversarial Network With Shallow Information Enhancement
url	https://doi.org/10.1109/JSTARS.2022.3209819 https://doaj.org/article/6bfd53d2ba154b6ea2d668b68af67f74 https://ieeexplore.ieee.org/document/9903573/ https://doaj.org/toc/2151-1535
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In addition, SIEGAN uses U-Net network as its discriminator to provide detailed feedback per pixel to the generator to improve the model's ability to identify image details. And the spectral–spatial total variation loss function is introduced to ensure the spectral–spatial reliability of the reconstructed images. The experimental results on Gaofen-1 data proved that compared with the state-of-the-art models, SIEGAN has achieved better SR reconstruction performance. 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Cosmic physics</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Xiangrong Zhang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Mingyang Wang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing</subfield><subfield code="d">IEEE, 2020</subfield><subfield code="g">15(2022), Seite 8529-8540</subfield><subfield code="w">(DE-627)581732634</subfield><subfield code="w">(DE-600)2457423-5</subfield><subfield code="x">21511535</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:15</subfield><subfield code="g">year:2022</subfield><subfield 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Super-Resolution Reconstruction of Remote Sensing Images Using Generative Adversarial Network With Shallow Information Enhancement

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