SAR-to-Optical Image Translation via Neural Partial Differential Equations

Authors: Mingjin Zhang, Chengyu He, Jing Zhang, Yuxiang Yang, Xiaoqi Peng, Jie Guo

IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental Experiments on the popular SEN1-2 dataset show that S2O-NPDE outperforms state-of-the-art methods in both objective metrics and visual quality.
Researcher Affiliation Collaboration 1State Key Laboratory of Integrated Services Networks, School of Telecommunications Engineering, Xidian University, Xi an 710071, China 2The University of Sydney, NSW 2006, Australia 3JD Explore Academy, China 4Hangzhou Dianzi University, Hangzhou 310018, China
Pseudocode No The paper provides architectural diagrams (Figures 1, 2, 3, 4) but does not include any pseudocode or algorithm blocks.
Open Source Code No The paper does not provide any statement or link indicating that the source code for the methodology is openly available.
Open Datasets Yes We select 1,600 high-quality SAR-optical pairs as the training dataset and 300 pairs as the test set (denoting as Test1) from the SEN1-2 datatset [Schmitt et al., 2018]
Dataset Splits No We select 1,600 high-quality SAR-optical pairs as the training dataset and 300 pairs as the test set (denoting as Test1) from the SEN1-2 datatset [Schmitt et al., 2018]... The learning rate is set to 2 10 4 and linearly reduced to zero from the 100th epoch.
Hardware Specification Yes All the experiments are implemented in Py Torch and on NVIDIA GTX 2080Ti GPUs.
Software Dependencies No The paper mentions 'Py Torch' as the implementation framework but does not specify a version number or other software dependencies with their versions.
Experiment Setup Yes We adopt ADAM optimizer with β1 = 0.5, β2 = 0.999 to train the model for 200 epochs with a batch size of 1. The learning rate is set to 2 10 4 and linearly reduced to zero from the 100th epoch.