A Dense Encoder–Decoder Network with Feedback Connections for Pan-Sharpening

To meet the need for multispectral images having high spatial resolution in practical applications, we propose a dense encoder–decoder network with feedback connections for pan-sharpening. Our network consists of four parts. The first part consists of two identical subnetworks, one each to extract f...

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Autores principales:	Weisheng Li, Minghao Xiang, Xuesong Liang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	convolutional neural network double-stream structure feedback encoder–decoder network dense connections Science Q
Acceso en línea:	https://doaj.org/article/a185dde662d44c2b930d3d38098e3ac6
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id	oai:doaj.org-article:a185dde662d44c2b930d3d38098e3ac6
record_format	dspace
spelling	oai:doaj.org-article:a185dde662d44c2b930d3d38098e3ac62021-11-25T18:53:44ZA Dense Encoder–Decoder Network with Feedback Connections for Pan-Sharpening10.3390/rs132245052072-4292https://doaj.org/article/a185dde662d44c2b930d3d38098e3ac62021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/22/4505https://doaj.org/toc/2072-4292To meet the need for multispectral images having high spatial resolution in practical applications, we propose a dense encoder–decoder network with feedback connections for pan-sharpening. Our network consists of four parts. The first part consists of two identical subnetworks, one each to extract features from PAN and MS images, respectively. The second part is an efficient feature-extraction block. We hope that the network can focus on features at different scales, so we propose innovative multiscale feature-extraction blocks that fully extract effective features from networks of various depths and widths by using three multiscale feature-extraction blocks and two long-jump connections. The third part is the feature fusion and recovery network. We are inspired by the work on U-Net network improvements to propose a brand new encoder network structure with dense connections that improves network performance through effective connections to encoders and decoders at different scales. The fourth part is a continuous feedback connection operation with overfeedback to refine shallow features, which enables the network to obtain better reconstruction capabilities earlier. To demonstrate the effectiveness of our method, we performed several experiments. Experiments on various satellite datasets show that the proposed method outperforms existing methods. Our results show significant improvements over those from other models in terms of the multiple-target index values used to measure the spectral quality and spatial details of the generated images.Weisheng LiMinghao XiangXuesong LiangMDPI AGarticleconvolutional neural networkdouble-stream structurefeedbackencoder–decoder networkdense connectionsScienceQENRemote Sensing, Vol 13, Iss 4505, p 4505 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	convolutional neural network double-stream structure feedback encoder–decoder network dense connections Science Q
spellingShingle	convolutional neural network double-stream structure feedback encoder–decoder network dense connections Science Q Weisheng Li Minghao Xiang Xuesong Liang A Dense Encoder–Decoder Network with Feedback Connections for Pan-Sharpening
description	To meet the need for multispectral images having high spatial resolution in practical applications, we propose a dense encoder–decoder network with feedback connections for pan-sharpening. Our network consists of four parts. The first part consists of two identical subnetworks, one each to extract features from PAN and MS images, respectively. The second part is an efficient feature-extraction block. We hope that the network can focus on features at different scales, so we propose innovative multiscale feature-extraction blocks that fully extract effective features from networks of various depths and widths by using three multiscale feature-extraction blocks and two long-jump connections. The third part is the feature fusion and recovery network. We are inspired by the work on U-Net network improvements to propose a brand new encoder network structure with dense connections that improves network performance through effective connections to encoders and decoders at different scales. The fourth part is a continuous feedback connection operation with overfeedback to refine shallow features, which enables the network to obtain better reconstruction capabilities earlier. To demonstrate the effectiveness of our method, we performed several experiments. Experiments on various satellite datasets show that the proposed method outperforms existing methods. Our results show significant improvements over those from other models in terms of the multiple-target index values used to measure the spectral quality and spatial details of the generated images.
format	article
author	Weisheng Li Minghao Xiang Xuesong Liang
author_facet	Weisheng Li Minghao Xiang Xuesong Liang
author_sort	Weisheng Li
title	A Dense Encoder–Decoder Network with Feedback Connections for Pan-Sharpening
title_short	A Dense Encoder–Decoder Network with Feedback Connections for Pan-Sharpening
title_full	A Dense Encoder–Decoder Network with Feedback Connections for Pan-Sharpening
title_fullStr	A Dense Encoder–Decoder Network with Feedback Connections for Pan-Sharpening
title_full_unstemmed	A Dense Encoder–Decoder Network with Feedback Connections for Pan-Sharpening
title_sort	dense encoder–decoder network with feedback connections for pan-sharpening
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/a185dde662d44c2b930d3d38098e3ac6
work_keys_str_mv	AT weishengli adenseencoderdecodernetworkwithfeedbackconnectionsforpansharpening AT minghaoxiang adenseencoderdecodernetworkwithfeedbackconnectionsforpansharpening AT xuesongliang adenseencoderdecodernetworkwithfeedbackconnectionsforpansharpening AT weishengli denseencoderdecodernetworkwithfeedbackconnectionsforpansharpening AT minghaoxiang denseencoderdecodernetworkwithfeedbackconnectionsforpansharpening AT xuesongliang denseencoderdecodernetworkwithfeedbackconnectionsforpansharpening
_version_	1718410608577085440

A Dense Encoder–Decoder Network with Feedback Connections for Pan-Sharpening

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