Xiangyu Rui, Xiangyong Cao, Li Pang, Zeyu Zhu, Zongsheng Yue, Deyu Meng
Pretrained diffusion model can be downloaded from
https://github.com/wgcban/ddpm-cd#arrow_forwardpre-trained-models--trainvaltest-logs
Chikusei: https://naotoyokoya.com/Download.html
Houston: https://hyperspectral.ee.uh.edu/?page id=459
Pavia: https://github.com/liangjiandeng/HyperPanCollection
Use data/generate_data.m to generate test data for Chikusei and Houston. Pavia can be directly downloaded for use.
run python3 demo_syn.py -res opt
Before you running the script, please first download the pre-trained diffusion model, put it to your file and change the --resume in demo_syn.py.
there are several options you can set:
-gpu: int
-dn: dataname,str. e.g. 'Chikusei_test'. The dataset should contain "HRMS", "LRMS" and "PAN".
-krtype: int. Set 0 for the first time in order to estimate kernel and srf. Set 1 if you have already save them in './estKR'.
-res: str. Set 'opt' for estimating the residual and 'no' for R=0.
Other options include eta1, eta2, scale, ks, step, accstep. Please refer to demo_syn.py.