Unsupervised Pan-Sharpening via Mutually Guided Detail Restoration
Authors: Huangxing Lin, Yuhang Dong, Xinghao Ding, Tianpeng Liu, Yongxiang Liu
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that the proposed method significantly outperforms existing unsupervised pan-sharpening methods. |
| Researcher Affiliation | Academia | Huangxing Lin1, Yuhang Dong2, Xinghao Ding2, Tianpeng Liu1*, Yongxiang Liu1 1College of Electronic Science, National University of Defense Technology, China 2School of Informatics, Xiamen University, China |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to its own source code for the methodology described. |
| Open Datasets | Yes | We utilize three satellite datasets, namely Gao Fen2 (GF2), Quick Bird (QB), and World View-3 (WV3), to assess the effectiveness of the proposed method. |
| Dataset Splits | No | The paper describes how reduced-resolution data is synthesized for evaluation and how full-resolution data is used for both training and testing, but it does not provide specific train/validation/test dataset splits with percentages, sample counts, or explicit standard split references for its own model training. |
| Hardware Specification | Yes | All experiments are performed on a single NVIDIA Ge Force GTX 3090 GPU. |
| Software Dependencies | No | The paper mentions using the 'Pytorch framework and the Adam optimizer' but does not specify their version numbers or other software dependencies with versions. |
| Experiment Setup | Yes | During the initial half of the training process, the learning rate is fixed at 0.0002, and in the latter half of the training, the learning rate linearly decays to 0. [...] To mitigate the adverse effects of Lgrad, we set β to 1 and λ to 3. |