# hyperspectral_deeplearning_review

**Repository Path**: algo_coding/hyperspectral_deeplearning_review

## Basic Information

- **Project Name**: hyperspectral_deeplearning_review
- **Description**: Code of paper "Deep Learning Classifiers for Hyperspectral Imaging: A Review"
- **Primary Language**: Unknown
- **License**: GPL-3.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-11-08
- **Last Updated**: 2021-11-08

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## 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 <algorithm>.py --dataset IP 
# Example:
python svm.py --dataset IP --tr_percent 0.15

# Fine-tuning (not recommended) <DENSENET121, MOBILENET, RESNET50, VGG16, VGG19>:
python pretrained_cnn.py --dataset IP --arch <architecture>
# Example:
python pretrained_cnn.py --dataset IP --arch VGG16

# Transfer learning <CNN1D, CNN2D, CNN2D40bands, CNN3D>, two steps:
python transfer_learning.py --dataset1 IP --dataset2 SV --arch <algorithm> --search_base_model
python transfer_learning.py --dataset1 IP --dataset2 SV --tr_samples 2 --use_val --arch <algorithm> --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 <algorithm>.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
```