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..
Unsupervised Pan-Sharpening via Mutually Guided Detail Restoration
Authors: Huangxing Lin, Yuhang Dong, Xinghao Ding, Tianpeng Liu, Yongxiang Liu
AAAI 2024 | Venue PDF | 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. |