The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.
A Multiscale and Multidepth Convolutional Neural Network for Remote Sensing Imagery Pan-Sharpening
Open accessAbstract
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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“Also, the multi-path CNN structures can fuse the extracted features in multiple depth of layers [22].”
From Automated placental abruption identification using semantic segmentation, quantitative features, SVM, ensemble and multi-path CNN (2023) · cites this paper as [22]
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Deep learning in remote sensing applications: A meta-analysis and review
Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., & Johnson, B. A. (2019) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.04.015 · Cited by 2,456
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Metadata
- Authors
- Yuan, Q., Wei, Y., Meng, X., Shen, H., & Zhang, L.
- Affiliations
- Wuhan University (CN)
- Ningbo University (CN)
- Venue
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- Published
- 2018-03
- Volume
- 11
- Issue
- 3
- Pages
- 978-989
- ISSN
- 1939-1404 (ISSN-L)
- 2151-1535
- 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
- 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 › Computer Science › Computer Vision and Pattern Recognition › 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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