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Hyperspectral Target Detection Based on Interpretable Representation Network, TGRS, 2023

Python 100.00%

htd-irn's Introduction

Hyperspectral Target Detection Based on Interpretable Representation Network (HTD-IRN), IEEE TGRS, 2023.

Files

`Main.py`, `Model.py`, `Train_Test.py`,`ts_generation.py`,`utils.py`.

Requirement

`python 3.6.13`, `torch 1.10.1`, NVIDIA Geforce RTX 2080Ti.

Network

HTD-IRN is a promising detector for hyperspectral imagery based on a deep subspace representation network with Uformer.

Run

run `Main.py`.

Note

1. Three states of `train`, `test`, or `parameter_selection` can be chosen in `Main.py`.

2. We provide a well-trained model of San Diego I, and you can test it directly.

3. For a new dataset, the optimal values of m and eta1 should be chosen first. So you can change the state to `parameter_selection` in `Main.py`.

Cite

@ARTICLE{shen2023hyperspectral,
  author={Shen, Dunbin and Ma, Xiaorui and Kong, Wenfeng and Liu, Jianjun and Wang, Jie and Wang, Hongyu},
  journal={IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING}, 
  title={Hyperspectral Target Detection Based on Interpretable Representation Network}, 
  year={2023},
  volume={},
  number={},
  pages={1-17},
  doi={10.1109/TGRS.2023.3302950}}

htd-irn's People

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