https://github.com/meiqihu/ACDA

Pytorch code of "Hyperspectral Anomaly Change Detection Based on Auto-encoder"
https://github.com/meiqihu/ACDA

Last synced: 3 months ago
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Pytorch code of "Hyperspectral Anomaly Change Detection Based on Auto-encoder"

• Host: GitHub
• URL: https://github.com/meiqihu/ACDA
• Owner: meiqihu
• License: gpl-3.0
• Created: 2022-04-27T07:33:04.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2023-05-05T03:35:10.000Z (over 1 year ago)
• Last Synced: 2024-07-23T04:35:54.954Z (4 months ago)
• Language: Python
• Size: 11.3 MB
• Stars: 43
• Watchers: 2
• Forks: 3
• Open Issues: 6
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-remote-sensing-change-detection - Hu M, Wu C, Zhang L, et al. Hyperspectral anomaly change detection based on autoencoder

README

        
# Hyperspectral anomaly change detection based on autoencoder

Pytorch implementation of JSTARS paper "Hyperspectral anomaly change detection based on autoencoder".

![image](https://github.com/meiqihu/ACDA/blob/main/Figure_ACDA.png)

# Paper

[Hyperspectral anomaly change detection based on autoencoder](https://ieeexplore.ieee.org/document/9380336)

Please cite our paper if you find it useful for your research.

>@ARTICLE{9380336,

  author={Hu, Meiqi and Wu, Chen and Zhang, Liangpei and Du, Bo},

  journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing}, 

  title={Hyperspectral Anomaly Change Detection Based on Autoencoder}, 

  year={2021},

  volume={14},

  number={},

  pages={3750-3762},

  doi={10.1109/JSTARS.2021.3066508}}

# Installation

Install Pytorch 1.10.2 with Python 3.6

# Dataset

Download the [dataset of Viareggio 2013]

链接：https://pan.baidu.com/s/1x_M0nRqV-jmugIB6MltmXQ 

提取码：ogum

[Dataset]: "Viareggio 2013" with de-striping, noise-whitening and spectrally binning

>img_data.mat:  

>>img_1(D1F12H1);   img_2(D1F12H2);    img_3(D2F22H2)

>pretrain_samples:     

>>un_idx_train1,un_idx_valid1,un_idx_train2,un_idx_valid2;  [acquired from the pre-detection result of USFA, Wu C, Zhang L, Du B. Hyperspectral anomaly change detection with slow feature analysis[J]. Neurocomputing, 2015, 151: 175-187.]

>groundtruth_samples:  

>>un_idx_train1,un_idx_valid1,un_idx_train2,un_idx_valid2;

>random_samples:     un_idx_train1,un_idx_valid1,un_idx_train2,un_idx_valid2;

# Usage

maincode.py

# More

🌷[Homepage](https://meiqihu.github.io/)🌷  

🔴[Google web](https://scholar.google.com.hk/citations?hl=zh-CN&user=jxyAHdkAAAAJ) 🔴 

🌏[ResearchGate](https://www.researchgate.net/profile/Humeiqi-humeiqi) 🌍