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Open AccessArticle

Two-Path Network with Feedback Connections for Pan-Sharpening in Remote Sensing

College of Electronics and Information Engineering, Sichuan University, Chengdu 610064, China
Department of Embedded System Engineering, Incheon National University, Incheon 22012, Korea
Applied Sciences Department, Université du Québec à Chicoutimi Chicoutimi, Saguenay, QC G7H 2B1, Canada
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Remote Sens. 2020, 12(10), 1674;
Received: 13 April 2020 / Revised: 15 May 2020 / Accepted: 19 May 2020 / Published: 23 May 2020
High-resolution multi-spectral images are desired for applications in remote sensing. However, multi-spectral images can only be provided in low resolutions by optical remote sensing satellites. The technique of pan-sharpening wants to generate high-resolution multi-spectral (MS) images based on a panchromatic (PAN) image and the low-resolution counterpart. The conventional deep learning based pan-sharpening methods process the panchromatic and the low-resolution image in a feedforward manner where shallow layers fail to access useful information from deep layers. To make full use of the powerful deep features that have strong representation ability, we propose a two-path network with feedback connections, through which the deep features can be rerouted for refining the shallow features in a feedback manner. Specifically, we leverage the structure of a recurrent neural network to pass the feedback information. Besides, a power feature extraction block with multiple projection pairs is designed to handle the feedback information and to produce power deep features. Extensive experimental results show the effectiveness of our proposed method.
Keywords: feedback; recurrent neural network; pan-sharpening; two-path feedback; recurrent neural network; pan-sharpening; two-path
MDPI and ACS Style

Fu, S.; Meng, W.; Jeon, G.; Chehri, A.; Zhang, R.; Yang, X. Two-Path Network with Feedback Connections for Pan-Sharpening in Remote Sensing. Remote Sens. 2020, 12, 1674.

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