Hyperspectral Image Analysis Simplified

Abstract

1. Introduction

The repository contains the implementation of different machine learning techniques such as classification and clustering on Hyperspectral and Satellite Imagery.

2. Objective

The repository contains the implementation of different machine learning techniques such as classification and clustering on Hyperspectral and Satellite Imagery.

This project demonstrates how Jupyter Notebook, TensorFlow, Pandas can be applied to a real-world Data Science problem.

4. Technology Stack

• 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.
• Visualizing pixels of the hyperspectral image.

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
• pip for dependencies
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/syamkakarla98/Hyperspectral_Image_Analysis_Simplified.git
cd Hyperspectral_Image_Analysis_Simplified

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by syamkakarla98 and published on GitHub under the GNU General Public License v3.0. Please follow the license terms and credit the original author when you use or modify this code.