Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Pan-Sharpening with Customized Transformer and Invertible Neural Network
Authors: Man Zhou, Jie Huang, Yanchi Fang, Xueyang Fu, Aiping Liu3553-3561
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments over different kinds of satellite datasets demonstrate that our method outperforms state-of-the-art algorithms both visually and quantitatively with fewer parameters and flops. Further, the ablation experiments also prove the effectiveness of the proposed customized long-range transformer and effective invertible neural feature fusion module for pan-sharpening. |
| Researcher Affiliation | Academia | Man Zhou2, 1 *, Jie Huang1 *, Yanchi Fang3, Xueyang Fu1, Aiping Liu1 1University of Science and Technology of China, China 2Hefei Institute of Physical Science, Chinese Academy of Sciences, China 3University of Toronto, Canada |
| Pseudocode | No | No pseudocode or clearly labeled algorithm block is present in the paper. |
| Open Source Code | No | The paper does not provide an unambiguous statement about releasing code or a direct link to a source-code repository for the methodology described. |
| Open Datasets | No | The paper mentions 'World View II, World View III , and Gao Fen2' datasets and states 'the basic information of which are listed in supplementary materials,' but does not provide a direct link, DOI, or specific citation for public access to these datasets. |
| Dataset Splits | No | The paper states 'For each satellite, we have hundreds of image pairs, and they are divided into two parts for training and test,' but does not explicitly mention a validation set split or its details. |
| Hardware Specification | Yes | We implement all our networks in Py Torch framework on the PC with a single NVIDIA Ge Force GTX 2080Ti GPU. |
| Software Dependencies | No | The paper mentions 'Py Torch framework' but does not specify its version number or versions of other key software dependencies. |
| Experiment Setup | Yes | In the training phase, they are optimized by Adam optimizer over 1000 epochs with a learning rate of 8 10 4 and a batch size of 4. In the training set, the MS images are cropped into patches with the size of 128 128 , and the corresponding PAN patches are with the size of 32 32. |