https://github.com/techut30/hyperspectral-image-band-selection-using-gwo
Hyperspectral band selection using Gray Wolf Optimization with KNN & Random Forest.
https://github.com/techut30/hyperspectral-image-band-selection-using-gwo
composite-images dimensionality-reduction feature-selection gwo-optimization-algorithm hyperspectral-image-selection knn-classification parameter-tuning random-forest-classifier
Last synced: 3 months ago
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Hyperspectral band selection using Gray Wolf Optimization with KNN & Random Forest.
- Host: GitHub
- URL: https://github.com/techut30/hyperspectral-image-band-selection-using-gwo
- Owner: techut30
- Created: 2024-11-03T17:24:05.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-11-23T17:21:15.000Z (7 months ago)
- Last Synced: 2025-01-20T21:47:20.761Z (5 months ago)
- Topics: composite-images, dimensionality-reduction, feature-selection, gwo-optimization-algorithm, hyperspectral-image-selection, knn-classification, parameter-tuning, random-forest-classifier
- Language: Python
- Homepage:
- Size: 5.91 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Hyperspectral-Image-Band-Selection-using-GWO
# Band Selection for Hyperspectral Image Analysis using Gray Wolf Optimization
This repository contains Python code for selecting the optimal bands from hyperspectral images using the **Gray Wolf Optimization (GWO)** algorithm. The GWO algorithm helps select the most informative spectral bands, enhancing the classification accuracy of hyperspectral images. Two methods are implemented for classification: **K-Nearest Neighbors (KNN)** and **Random Forest** classifiers.
## Overview
The repository includes two main files:
- `GWO_KNN.py`: Uses a K-Nearest Neighbors (KNN) classifier for band selection.
- `GWO_RandomForest.py`: Alternative approach that uses a Random Forest classifier for band selection.
Both files utilize Gray Wolf Optimization to identify the optimal subset of spectral bands for classification.
## Requirements
- Python 3.x
- NumPy
- Scikit-Learn
- Scipy
- Matplotlib
- `GrayWolfOpt` (Gray Wolf Optimization algorithm, should be implemented or imported)
- `CompositeImages` and `ImagePlots` (modules for visualization)
## Files
### `GWO_KNN.py`
This file performs band selection using the GWO algorithm and classifies using the **K-Nearest Neighbors** classifier.
1. **Objective Function**: Defines the objective function for GWO. The function minimizes the classification error rate based on selected spectral bands.
2. **Dataset Loading**: Loads the hyperspectral dataset (`Indian_pines_corrected.mat` for data and `Indian_pines_gt.mat` for labels).
3. **Optimization**: The GWO algorithm optimizes the selected bands to minimize the error rate.
4. **Visualization**: Selected bands are visualized as a composite image.
#### Usage
```python
python GWO_KNN.py
```
### `GWO_RandomForest.py`
An alternative approach where band selection is performed using the **Random Forest classifier** instead of KNN. This script is structured similarly to main.py, with the following differences:
1. **Classifier**: Uses RandomForestClassifier with 100 estimators to evaluate the fitness of the selected bands.
2. **Parameters**: Adjusted parameters for GWO, including a higher number of bands and wolves.
3. **Visualization**: Visualizes the selected bands as a composite image.
### Usage
```python
python GWO_RandomForest.py
```
## Code Explanation
### Objective Function for Band Selection
The objective function in both scripts takes in a set of selected bands, trains a classifier (KNN or Random Forest), and returns the error rate based on classification accuracy. The goal is to minimize this error rate, which represents the fitness score for GWO.
### DataSet Loading
The dataset used is the Indian Pines hyperspectral dataset. The data is reshaped to (pixels, bands) format, and unlabeled pixels (background) are removed for accurate band selection.
### Gray Wolf Optimization
The GWO algorithm optimizes the band selection process. The algorithm is configured with:
**dim**: Number of bands to select.
**lb and ub**: Lower and upper bounds for band indices.
**num_wolves and max_iter**: Parameters defining the number of wolves and iterations.
### Timer
A timer function displays the elapsed time for the optimization process.
## Example Outputs
Upon running each script, the output includes:
**Best Bands Selected**: The optimal subset of bands for classification.
**Best Score (Error Rate)**: The minimized classification error achieved.
**Composite Image**: A visualization of the selected bands.
## References
1. Gray Wolf Optimizer: Inspired by the social hierarchy and hunting strategy of gray wolves.
2. Indian Pines Dataset: Commonly used hyperspectral dataset for classification tasks.