/ADASR

ADASR official implementation ADASR: An Adversarial Auto-Augmentation Framework for Hyperspectral and Multispectral Data Fusion

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ADASR: An Adversarial Auto-Augmentation Framework for Hyperspectral and Multispectral Data Fusion

This repo is the official implementation of "ADASR: An Adversarial Auto-Augmentation Framework for Hyperspectral and Multispectral Data Fusion"

Our paper is accpeted by IEEE Geoscience and Remote Sensing Letters (GRSL).

The early access version can be downloaded in arxiv.

Fig.1. overview. (a) The adversarial auto-augmentation framewor (b) The design of our data augmentor G.

checkpoints

This folder is used to store the training results and a folder named houston18_5_S1=0.001_20000_10000_S2=0.001_30000_20000_S3=6e-05_15000_5000 is given as a example.

  • convolution_hr2msi.pth is the trained result of SpeDnet, PSF.pth is the trained result of SpaDnet, and spectral_upsample.pth is the trained result of SpeUnet.

  • opt.txt is used to store the training configuration.

  • log.txt is used to store the training process result.

  • My_Out.mat is the final reconstructed HHSI.

data

This folder is used to store the ground true HHSI and corresponding spectral response of multispectral imager. The HSI data used in 2018 IEEE GRSS Data Fusion Contest and spectral response of WorldView 2 multispectral imager are given as a example here.

How to run our code

  • Requirements: codes of networks were tested using PyTorch 1.9.0 version (CUDA 11.1) in Python 3.8.10 on Linux system. For the required packages, please using command：pip install -r requirement.txt

  • Parameters: all the parameters need fine-tunning can be found in config.py, including the learning rate decay strategy of three training stages.

  • Data: put your HSI data and MSI spectral reponse in ./data/data_name and ./data/spectral_response, respectively.The HSI data used in 2018 IEEE GRSS Data Fusion Contest and spectral response of WorldView 2 multispectral imager are given as a example here.

  • Run: just simply run train.py after adjust the parameters in config.py.

  • Results: one folder named dataname_SF_S1=x1_y1_z1_S2=x2_y2_z2 will be generated once train.py is run and all the results will be stored in the new folder. A folder named houston18_5_S1=0.0008_40000_10000_S2=0.0013_200000_20000 is given as a example here.

Acknowledgement

The spatial_downsample.py(SpaDnet), spectral_downsample.py(SpeDnet), spectral_upsample.py(SpeUnet) is modified from UDALN.

Jiaxin Li, Ke Zheng, Jing Yao, Lianru Gao, Danfeng Hong. Deep unsupervised blind hyperspectral and multispectral data fusion[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 1-5.

Other description will be released soon.