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https://github.com/syamkakarla98/dimensionality-reduction-and-classification-on-hyperspectral-images-using-python
In this repository, You can find the files which implement dimensionality reduction on the hyperspectral image(Indian Pines) with classification.
https://github.com/syamkakarla98/dimensionality-reduction-and-classification-on-hyperspectral-images-using-python
classification dimensionality-reduction hacktoberfest hacktoberfest2020 hyperspectral-image-classification indian-pines matplotlib numpy pandas pca principal-components python-3
Last synced: about 1 month ago
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In this repository, You can find the files which implement dimensionality reduction on the hyperspectral image(Indian Pines) with classification.
- Host: GitHub
- URL: https://github.com/syamkakarla98/dimensionality-reduction-and-classification-on-hyperspectral-images-using-python
- Owner: syamkakarla98
- License: mit
- Created: 2018-06-16T03:44:59.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2020-10-11T08:39:13.000Z (about 4 years ago)
- Last Synced: 2023-03-05T11:18:12.693Z (almost 2 years ago)
- Topics: classification, dimensionality-reduction, hacktoberfest, hacktoberfest2020, hyperspectral-image-classification, indian-pines, matplotlib, numpy, pandas, pca, principal-components, python-3
- Language: Jupyter Notebook
- Homepage:
- Size: 8.66 MB
- Stars: 70
- Watchers: 3
- Forks: 31
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
- Code of conduct: CODE_OF_CONDUCT.md
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README
# Dimensionality reduction and classification on [Hyperspectral Image](http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes) Using Python
## Authors
* [**DR.T.Hitendra Sarma**](https://scholar.google.co.in/citations?user=8Frh6IQAAAAJ&hl=en)
* [**Syam Kakarla**](https://www.linkedin.com/in/syam-kakarla/)
### Prerequisites
The prerequisites to better understand the code and concept are:
```
* Python
* MatLab
* Linear Algebra
```
### Installation
* This project is fully based on python. So, the necessary modules needed for computaion are:
```
* Numpy
* Sklearn
* Matplotlib
* Pandas
```
* The commands needed for installing the above modules on windows platfom are:
```python
pip install numpy
pip install sklearn
pip install matplotlib
pip install pandas
```
* we can verify the installation of modules by importing the modules. For example:
```python
import numpy
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
import pandas as pd
```
### Results
* Here we are performing the the **dimensionality reduction** on one of the widely used **hyperspectral image** [Indian Pines](http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes)
1. The result of the [indian_pines_pca.py](
https://github.com/syamkakarla98/Dimensionality-reduction-and-classification-on-Hyperspectral-Images-Using-Python/blob/master/indian_pines_after_pca.csv) is shown below:
* It initial result is a bargraph for the first **10 Pricipal Components according** to their _variance ratio's_ :
Since, the initial two principal COmponents have high variance. so, we will select the initial two PC'S.
* It second result is a scatter plot for the first **10 Pricipal Components** is :
* The above program resullts a dimensionally reduced [csvfile](
https://github.com/syamkakarla98/Dimensionality-reduction-and-classification-on-Hyperspectral-Images-Using-Python/blob/master/indian_pines_after_pca.csv) .
2. The result of the [indian_pines_knnc.py](https://github.com/syamkakarla98/Dimensionality-reduction-and-classification-on-Hyperspectral-Images-Using-Python/blob/master/Indian_pines_knnc.py) is given below:
* The above program will classify the Indian Pines dataset before **Principal Component Analysis(PCA)**. The classifier here used for classification is [K-Nearest Neighbour Classifier (KNNC)](http://scikitlearn.org/stable/auto_examples/neighbors/plot_classification.html)
* The time taken for classification is:
* Then the classification accuracy of indian pines dataset before **PCA** is:
3. The result of the [indian_pines_knnc_after_pca.py](
https://github.com/syamkakarla98/Dimensionality-reduction-and-classification-on-Hyperspectral-Images-Using-Python/blob/master/Indian_pines_knnc_after_pca.py)
* Then the resultant classification accuracy of indian pines dataset after **PCA** is:
### Conclusion :
* By performing **PCA** on the corrected indian pines dataset results **100 Principal Components(PC'S)**.
* since, the initial two Principal Components(PC'S) has **92.01839071674918** variance ratio. we selected two only.
* Initially the dataset contains the dimensions **21025 X 200** is drastically reduced to **21025 X 2** dimensions.
* The time taken for classification before and after Principal Component Analysis(PCA) is:
| Dataset | Accuracy | Time Taken |
| ------------- |:-----------: | ----------:|
| Before PCA | 72.748890 | 17.6010 |
| After PCA | 60.098187 | 0.17700982 |
* Hence, the **time** has been reduced with a lot of difference and the **classification accuracy(C.A)** also reduced but the C.A can increased little bit by varying the 'k' value.
## License
This project is licensed under the MIT License - see the [LICENSE.md](https://github.com/syamkakarla98/Dimensionality-reduction-and-classification-on-Hyperspectral-Images-Using-Python/blob/master/LICENSE.md) file for details