Satellite Imagery NDVI Classification and Feature Extraction

This Python script automates the process of NDVI calculation, land-use classification, and feature extraction from satellite imagery. It uses the Red and Near-Infrared (NIR) bands of satellite images to calculate NDVI, classifies the image into categories (vegetation, water, barren land), and extracts polygons of each category as a shapefile.

Key Features:

1. NDVI Calculation: Computes the Normalized Difference Vegetation Index (NDVI) using satellite imagery.
2. Image Classification: Classifies NDVI values into vegetation, water, and barren land categories.
3. Feature Extraction: Converts the classified raster data into vector polygons for vegetation areas, water bodies, and barren land, and saves them as a shapefile.
4. User-Friendly GUI: Provides an easy-to-use graphical user interface (GUI) for users to select files and save results without needing programming knowledge.

Libraries Used:

• rasterio: For reading and processing raster data (satellite imagery).
• numpy: For numerical calculations and data manipulation.
• matplotlib: For visualizing NDVI and classification results.
• geopandas: For creating and saving shapefiles from the classified data.
• tkinter: For creating the GUI that allows users to select files and directories.

How It Works:

1. Step 1: User selects the Red (Band 4) and Near-Infrared (Band 5) satellite bands.
2. Step 2: The script calculates NDVI from these bands.
3. Step 3: The NDVI image is classified into three categories:
   • Vegetation (NDVI > 0.3)
   • Water (NDVI < 0.1)
   • Barren Land (0.0 <= NDVI < 0.3)
4. Step 4: The classified image is saved as a GeoTIFF.
5. Step 5: Polygons representing the classified categories (e.g., vegetation areas) are extracted and saved as a shapefile for further analysis in GIS tools (e.g., QGIS or ArcGIS).

How to Run the Script:

Requirements:

• Python 3.x
• The following Python libraries:
  • rasterio
  • numpy
  • matplotlib
  • geopandas
  • tkinter (comes with Python)

You can install the required libraries using pip:

bash

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pip install rasterio numpy matplotlib geopandas

Running the Script:

1. Clone this repository to your local machine.

2. Run the script using Python:

   bash

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   python satellite_processing.py

   This will launch the GUI that will guide you through:

   • Selecting the Red and NIR bands for NDVI calculation.
   • Saving the NDVI GeoTIFF.
   • Classifying the NDVI image into vegetation, water, and barren land.
   • Extracting polygons and saving them as a shapefile.

Expected Outputs:

1. NDVI GeoTIFF: A GeoTIFF file containing the NDVI values.
2. Classified GeoTIFF: A GeoTIFF file showing the classification results (vegetation, water, barren land).
3. Shapefile: A shapefile containing the polygons for vegetation areas (or other categories) extracted from the classified image.

Example Outputs:

• NDVI GeoTIFF: Contains normalized values from -1 to 1, where positive values represent vegetation.
• Classified GeoTIFF: A categorized image where different land use classes (vegetation, water, barren land) are represented with unique integer values.
• Shapefile: Contains vector polygons of the classified land use categories, which can be opened in QGIS or any GIS tool.

Use Cases:

• Environmental Monitoring: Detect deforestation, track vegetation health, or monitor water bodies.
• Agriculture: Monitor crop health and growth.
• Urban Planning: Analyze land-use patterns, including urban and vegetation areas.
• Water Resources: Study the dynamics of water bodies over time.
• Disaster Response: Assess the impact of natural disasters such as floods or wildfires on vegetation and land.

License:

This project is licensed under the MIT License - see the LICENSE file for details.