Pan-Sharpening with Customized Transformer and Invertible Neural Network
Authors: Man Zhou, Jie Huang, Yanchi Fang, Xueyang Fu, Aiping Liu3553-3561
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | 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. |