A multiscale and multidepth convolutional neural network for remote sensing imagery pan-sharpening

Yuan, Qiangqiang; Wei, Yancong; Meng, Xiangchao; Shen, Huanfeng; Zhang, Liangpei

doi:10.1109/jstars.2018.2794888

Cited by 649Advanced Image Fusion TechniquesSatellite Image Processing and PhotogrammetryAdvanced Image Processing Techniques

A multiscale and multidepth convolutional neural network for remote sensing imagery pan-sharpening

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Yuan, Qiangqiang, Wei, Yancong, Meng, Xiangchao, Shen, Huanfeng, Zhang, Liangpei·2018·IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

DOI 10.1109/jstars.2018.2794888

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Abstract

Pan-sharpening is a fundamental and significant task in the field of remote sensing imagery processing, in which high-resolution spatial details from panchromatic images are employed to enhance the spatial resolution of multispectral (MS) images. As the transformation from low spatial resolution MS image to high-resolution MS image is complex and highly nonlinear, inspired by the powerful representation for nonlinear relationships of deep neural networks, we introduce multiscale feature extraction and residual learning into the basic convolutional neural network (CNN) architecture and propose the multiscale and multidepth CNN for the pan-sharpening of remote sensing imagery. Both the quantitative assessment results and the visual assessment confirm that the proposed network yields high-resolution MS images that are superior to the images produced by the compared state-of-the-art methods.

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Authors: Yuan, Q., Wei, Y., Meng, X., Shen, H., & Zhang, L.
Affiliations: Wuhan University (CN)
Ningbo University (CN)
ORCID: Yuan, Qiangqiang
Wei, Yancong
Meng, Xiangchao
Shen, Huanfeng
Zhang, Liangpei
Venue: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2018-03
Volume: 11
Issue: 3
Pages: 978-989
ISSN: 1939-1404 (ISSN-L)
2151-1535
DOI: 10.1109/jstars.2018.2794888
OpenAlex: W2963183385
Cited by: 649
References: 54
Citations by year: 2026: 70 · 2025: 86 · 2024: 103 · 2023: 95 · 2022: 96 · 2021: 84 · 2020: 60 · 2019: 40 · 2018: 15
FWCI: 37.70
Type: journal article
Language: en
Open access: green
arxiv.org
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (publishedVersion)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University)
Funders: National Key Research and Development Program of China (2016YFB0501403)
National Natural Science Foundation of China (41431175)
Fundamental Research Funds for the Central Universities (2042017kf0180)
Natural Science Foundation of Hubei Province (ZRMS2016000241)
Topics: Physical Sciences › Engineering › Media Technology › Advanced Image Fusion Techniques
Physical Sciences › Engineering › Ocean Engineering › Satellite Image Processing and Photogrammetry
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image Processing Techniques
Keywords: pansharpening, multispectral imagery, panchromatic imagery, multi-scale feature extraction, residual learning, spatial resolution enhancement

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A multiscale and multidepth convolutional neural network for remote sensing imagery pan-sharpening

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