Methods › Computer Vision › Convolutional Neural Networks › DRPNN
Deep Residual Pansharpening Neural Network
DRPNN
Introduced by Yancong Wei et al. in Boosting the accuracy of multi-spectral image pan-sharpening by learning a deep residual network
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
In the field of fusing multi-spectral and panchromatic images (Pan-sharpening), the impressive effectiveness of deep neural networks has been recently employed to overcome the drawbacks of traditional linear models and boost the fusing accuracy. However, to the best of our knowledge, existing research works are mainly based on simple and flat networks with relatively shallow architecture, which severely limited their performances. In this paper, the concept of residual learning has been introduced to form a very deep convolutional neural network to make a full use of the high non-linearity of deep learning models. By both quantitative and visual assessments on a large number of high quality multi-spectral images from various sources, it has been supported that our proposed model is superior to all mainstream algorithms included in the comparison, and achieved the highest spatial-spectral unified accuracy.
Papers archive 2025-07-28
2 shown of 2, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Pansharpening by convolutional neural networks in the full resolution framework 16 Nov 2021 · 2 repositories · arXiv:2111.08334
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Boosting the accuracy of multi-spectral image pan-sharpening by learning a deep residual network 22 May 2017 · 0 repositories · arXiv:1705.07556
Tasks archive 2025-07-28
4 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Image Super-Resolution | 1 |
| Pansharpening | 1 |
| Super-Resolution | 1 |
| satellite image super-resolution | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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