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https://github.com/mhaut/hyperspectral_deeplearning_review

Code of paper "Deep Learning Classifiers for Hyperspectral Imaging: A Review"
https://github.com/mhaut/hyperspectral_deeplearning_review

classification deeplearning hyperspectral hyperspectral-image-classification review

Last synced: 27 days ago
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Code of paper "Deep Learning Classifiers for Hyperspectral Imaging: A Review"

Host: GitHub
URL: https://github.com/mhaut/hyperspectral_deeplearning_review
Owner: mhaut
License: gpl-3.0
Created: 2019-09-07T19:10:35.000Z (almost 5 years ago)
Default Branch: master
Last Pushed: 2020-03-09T07:35:11.000Z (over 4 years ago)
Last Synced: 2024-02-24T15:35:09.374Z (4 months ago)
Topics: classification, deeplearning, hyperspectral, hyperspectral-image-classification, review
Language: Python
Homepage:
Size: 290 KB
Stars: 172
Watchers: 6
Forks: 53
Open Issues: 1
Metadata Files:
- Readme: README.md
- License: LICENSE

Lists

awesome-hyperspectral-deep-learning - [html

README

## Deep Learning Classifiers for Hyperspectral Imaging: A Review
![](https://img.shields.io/github/stars/mhaut/hyperspectral_deeplearning_review.svg) ![](https://img.shields.io/github/forks/mhaut/hyperspectral_deeplearning_review.svg) ![](https://img.shields.io/github/issues/mhaut/hyperspectral_deeplearning_review.svg)
The Code for "Deep Learning Classifiers for Hyperspectral Imaging: A Review".
[https://www.sciencedirect.com/science/article/pii/S0924271619302187]
```
M. E. Paoletti, J. M. Haut, J. Plaza and A. Plaza.
Deep Learning Classifiers for Hyperspectral Imaging: A Review
International Society for Photogrammetry and Remote Sensing
DOI: 10.1016/j.isprsjprs.2019.09.006
vol. 158, pp. 279-317, December 2019.
```

![reviewHSI](https://github.com/mhaut/hyperspectral_deeplearning_review/blob/master/images/paviaclasf.png)

### Example of use
```
# Without datasets
git clone https://github.com/mhaut/hyperspectral_deeplearning_review/

# With datasets
git clone --recursive https://github.com/mhaut/hyperspectral_deeplearning_review/
cd HSI-datasets
python join_dsets.py
```

### Run code
Go to algorithms folder and run
```
# Training from scratch
python .py --dataset IP
# Example:
python svm.py --dataset IP --tr_percent 0.15

# Fine-tuning (not recommended) :
python pretrained_cnn.py --dataset IP --arch
# Example:
python pretrained_cnn.py --dataset IP --arch VGG16

# Transfer learning , two steps:
python transfer_learning.py --dataset1 IP --dataset2 SV --arch --search_base_model
python transfer_learning.py --dataset1 IP --dataset2 SV --tr_samples 2 --use_val --arch --use_transfer_learning
# Example:
python transfer_learning.py --dataset1 IP --dataset2 SV --arch CNN2D40bands --search_base_model
python transfer_learning.py --dataset1 IP --dataset2 SV --tr_samples 2 --use_val --arch CNN2D40bands --use_transfer_learning
```

#### Other parameters
Dimensionality reduction **- - components** [number]
```
python .py --dataset IP --components 40
```
You can change the proposed parameters **- - set_parameters** [parameters]
```
python svm.py --dataset IP --set_parameters --C 2 --g 0.01
```
You can use validation set **- - use_val** by default is 10%, you can change it **- -use_val - -val_percent** [percent]
```
python cnn1d.py --dataset IP --use_val --val_percent 0.10
```
Example:
```
python cnn1d.py --dataset IP --components 40 --set_parameters --epochs 100 --batch_size 32--use_val --val_percent 0.10
```