Maximum Likelihood Classification (MLC) with Python

Welcome to this repository where we explore satellite imagery processing using Python! Our primary tool is a script designed to perform Maximum Likelihood Classification (MLC), a statistical method used in remote sensing for classifying satellite imagery.

What is Maximum Likelihood Classification?

Maximum Likelihood Classification is a probabilistic classification method in remote sensing. It's used for classifying satellite imagery data by assigning each pixel to a class based on statistical characteristics, aiming to accurately categorize land cover or other features in satellite images.

About This Python Script

This script is perfect for those looking to apply MLC on satellite raster imagery. It's user-friendly, requiring only two inputs:

1. A satellite raster image file.
2. A training dataset in Shapefile format.

How It Works

• Data Loading: Load the satellite raster data and training data from a Shapefile.
• Data Preparation: Process these inputs to create a model based on the training data.
• MLC Processing: Apply the Maximum Likelihood Classification algorithm to classify each pixel in the raster image.
• Output Generation: Produce a new raster file with the classified data.

Key Features

• User-Friendly: Simple inputs for ease of use.
• Versatile: Suitable for various types of satellite imagery.
• Efficient: Optimized for quick processing times.

Getting Started

Clone this repository to your local machine and ensure you have the required libraries (numpy, scipy, rasterio, and geopandas) installed.

Prerequisites

• Python 3.x
• Libraries: numpy, scipy, rasterio, geopandas

Running the Script

1. Place your satellite raster image and Shapefile in the designated file paths.
2. Run the script using Python.
3. Find the output as a classified raster image based on your input data.

Benefits of Using This Script

• Accessibility: Makes MLC techniques available to a wider audience.
• Customization: Easily modifiable for different project needs.
• Educational Value: Ideal for learning about MLC in remote sensing.

Contributing

We welcome contributions, and your involvement is what makes the community amazing. To contribute:

1. Fork the Project.
2. Create your Feature Branch (git checkout -b feature/AmazingFeature).
3. Commit your Changes (git commit -m 'Add some AmazingFeature').
4. Push to the Branch (git push origin feature/AmazingFeature).
5. Open a Pull Request against the main branch.

License

Distributed under the MIT License. See LICENSE for more information.

Contact

Your Name: Akhil Chhibber

LinkedIn: https://www.linkedin.com/in/akhilchhibber/

Medium Blogs: https://medium.com/@akhil.chibber