Methods › Computer Vision › Convolutional Neural Networks › PanNet

Pansharpening Network

PanNet

4 papers tagged archive 2025-07-28

Introduced by Junfeng Yang et al. in PanNet: A Deep Network Architecture for Pan-Sharpening

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

We propose a deep network architecture for the pansharpening problem called PanNet. We incorporate domain-specific knowledge to design our PanNet architecture by focusing on the two aims of the pan-sharpening problem: spectral and spatial preservation. For spectral preservation, we add up-sampled multispectral images to the network output, which directly propagates the spectral information to the reconstructed image. To preserve the spatial structure, we train our network parameters in the high-pass filtering domain rather than the image domain. We show that the trained network generalizes well to images from different satellites without needing retraining. Experiments show significant improvement over state-of-the-art methods visually and in terms of standard quality metrics.

PaperSource

Papers archive 2025-07-28

4 shown of 4, 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.

Tasks archive 2025-07-28

5 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Pansharpening3
Deep Learning1
Image Super-Resolution1
Super-Resolution1
satellite image super-resolution1

Usage over time archive 2025-07-28

Papers per year tagged with PanNet: 2017 to 2023, peak 1 1 0 2017: 1 paper 2017 2018: 0 papers 2018 2019: 0 papers 2019 2020: 0 papers 2020 2021: 1 paper 2021 2022: 1 paper 2022 2023: 1 paper 2023
Papers per year the archive tags with this method, by the paper's archive date (4 dated). Bars are counts, not a trend claim.

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

Convolutional Neural Networks

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