https://github.com/syamkakarla98/hyperspectral_image_analysis_simplified

The repository contains the implementation of different machine learning techniques such as classification and clustering on Hyperspectral and Satellite Imagery.
https://github.com/syamkakarla98/hyperspectral_image_analysis_simplified

classification data-analysis data-science dimensionality-reduction hacktoberfest hyperspectral hyperspectral-image-classification hyperspectral-images indian-pines-dataset machine-learning matplotlib-pyplot pandas plotly python python3 remote-sensing satellite-imagery satellite-images tensorflow turorial

Last synced: 7 days ago
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The repository contains the implementation of different machine learning techniques such as classification and clustering on Hyperspectral and Satellite Imagery.

• Host: GitHub
• URL: https://github.com/syamkakarla98/hyperspectral_image_analysis_simplified
• Owner: syamkakarla98
• License: gpl-3.0
• Created: 2019-12-13T05:17:07.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2023-07-06T21:27:28.000Z (over 1 year ago)
• Last Synced: 2025-01-08T12:07:25.717Z (14 days ago)
• Topics: classification, data-analysis, data-science, dimensionality-reduction, hacktoberfest, hyperspectral, hyperspectral-image-classification, hyperspectral-images, indian-pines-dataset, machine-learning, matplotlib-pyplot, pandas, plotly, python, python3, remote-sensing, satellite-imagery, satellite-images, tensorflow, turorial
• Language: Jupyter Notebook
• Homepage:
• Size: 21.4 MB
• Stars: 231
• Watchers: 2
• Forks: 48
• Open Issues: 4
• Metadata Files:
  • Readme: README.md
  • Contributing: Contributing.md
  • License: LICENSE

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README

        
# Hyper Spectral Image(HSI) Analysis Simplified

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#### The repository contains the implementation of different machine learning techniques on Hyperspectral and satellite Imagery analysis. Find more articles from [here](https://syamkakarla.medium.com/).

1.[ **Basics**](https://github.com/syamkakarla98/Hyperspectral_Image_Analysis_Simplified/blob/master/Basics.ipynb) - This notebook fatures:

  * **Introduction**

  * **Downloading HSI**

  * **Reading** the hyperspecral image.

  * **Visualizing the bands** of the hyperspectral image.

  * **Visualizing ground truth** of the image.

  * **Extracting pixels** of the hyperspectral image.

  * **Visualizing spectral signatures** of the hyperspectral image.

2.[ **Data Analysis**](https://github.com/syamkakarla98/Hyperspectral_Image_Analysis_Simplified/blob/master/Data%20Analysis.ipynb) - This notebook fatures data anlysis of the indian pines hyperspectral image:

  * **Visualizing pixels** of the hyperspectral image.

  * **Bar plot** w.r.t _class labels_ of the hyperspectral image.

  * **Box Plot** w.r.t the _class labels_ and _bands_ of hyperspecral image.

  * **Distribution Plot** w.r.t the _bands_ of hyperspecral image.

3.[**Exploratory Data Analysis (EDA) on Satellite Imagery Using EarthPy**](https://towardsdatascience.com/exploratory-data-analysis-eda-on-satellite-imagery-using-earthpy-c0e186fe4293)

4.**Dimensionality Reduction**

  * Check this article entitled [Dimensionality Reduction in Hyperspectral Images using Python](https://towardsdatascience.com/dimensionality-reduction-in-hyperspectral-images-using-python-611b40b6accc) and [*code*](https://github.com/syamkakarla98/Hyperspectral_Image_Analysis_Simplified/blob/master/Articles/Dimensionality_Reduction_on%C2%A0HSI_using_PCA.ipynb).

  

  

  * [ **PCA + SVM**](https://github.com/syamkakarla98/Hyperspectral_Image_Analysis_Simplified/blob/master/PCA%2BSVM.ipynb) - This notebook implements the following _machine learning_ techniques on the indian pines dataset.

    * **Dimensionality Rreduction**: The _principal component analysis(PCA)_ is used to reduce the dimensions of the dataset.

    * **Classifier**: The _support vector machine(SVM)_ classifier is used to classsify the pixels of the HSI with classification report and the confusion matrix, classification map of the classifier is visualized.

  

 * [ **Kernel PCA + SVM**](https://github.com/syamkakarla98/Hyperspectral_Image_Analysis_Simplified/blob/master/kernel%20PCA%2BSVM.ipynb)  - This notebook implements the following _machine learning_ techniques on the indian pines dataset.

 

    * **Dimensionality Rreduction**: The _Kernel principal component analysis(PCA)_  with '**rbf kernel**' is used to reduce the dimensionality of the dataset.

    * **Classifier**: The _support vector machine(SVM)_ classifier is used to classsify the pixels of the HSI with classification report and the confusion matrix, classification map of the classifier is visualized.

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