Local-Global Transformer Enhanced Unfolding Network for Pan-sharpening
Authors: Mingsong Li, Yikun Liu, Tao Xiao, Yuwen Huang, Gongping Yang
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experimental results on three satellite data sets demonstrate the effectiveness and efficiency of LGTEUN compared with state-of-the-art (SOTA) methods. |
| Researcher Affiliation | Academia | Mingsong Li1 , Yikun Liu1 , Tao Xiao1 , Yuwen Huang2 , and Gongping Yang1 1School of Software, Shandong University, Jinan, China 2School of Computer, Heze University, Heze, China |
| Pseudocode | No | The paper provides architectural diagrams and textual descriptions of the algorithm, but it does not include a formal pseudocode block or an algorithm box. |
| Open Source Code | Yes | The source code is available at https://github.com/ lms-07/LGTEUN. |
| Open Datasets | Yes | For the MS pan-sharpening, an 8-band MS data set acquired by the World View-3 sensor 2 and two 4-band MS data sets acquired by World View-2 2 and Gao Fen-2 sensors are adopted for experimental analysis. ... 2https://www.l3harris.com/all-capabilities/ high-resolution-satellite-imagery |
| Dataset Splits | No | The paper states: 'Each data set is further split into non-overlapping subsets for training (about 1000 Lr MS/PAN/GT image pairs) and testing (about 140 Lr MS/PAN/GT image pairs).', but it does not explicitly mention a distinct validation split. |
| Hardware Specification | Yes | All the experiments are conducted in Py Torch framework with a single NVIDIA Ge Force GTX 3090 GPU. |
| Software Dependencies | No | The paper mentions that 'All the experiments are conducted in Py Torch framework' but does not specify a version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | The end-to-end training of LGTEUN is supervised by mean absolute error (MAE) loss between the network output ZK and the GT Hr MS image. It trains 130 epochs for the 8-band data set, and 1000 epochs for the two 4band data sets. The Adam optimizer with β1 = 0.9 and β2 = 0.999 is employed for model optimization, and the batch size is set as 4. The initial learning rate is 1.5 10 3, and decays by 0.85 every 100 epochs. |