satfarm 0.1.2

A Python package for satellite image processing with fluent interface

satfarm

A Python package for satellite image processing with a fluent interface, built on top of rasterio and xarray.

🌟 Features

• Raster data I/O: Support for GeoTIFF, PNG, and other common formats
• Coordinate reference system: Transformation and resampling capabilities
• Band operations: Spectral index calculations and band manipulations
• Image rendering: Visualization with customizable color maps
• Geometric operations: Clipping, shrinking, and spatial transformations
• Fluent interface: Chain operations together for readable, maintainable code

📦 Installation

pip install satfarm

Development Installation

git clone https://github.com/yourusername/satfarm.git
cd satfarm
pip install -e ".[dev]"

🚀 Quick Start

from satfarm import SatImage
import numpy as np

# Basic usage
processor = SatImage()

# Load and process satellite imagery
result = (processor
    .read_tif("path/to/satellite_image.tif")
    .change_pixel_dtype("float32")
    .change_nodata(new_nodata=np.nan, old_nodata=0)
    .reproject("EPSG:4326")
    .shrink(distance=30)
    .set_band_alias(["red", "green", "blue", "nir"])
    .to_tif("processed_image.tif")
)

print(result)

📖 Examples

Spectral Index Calculation

from satfarm import SatImage

# Load image and set band aliases
simage = (SatImage()
    .read_tif("multispectral_image.tif")
    .set_band_alias(["blue", "green", "red", "nir"])
)

# Calculate vegetation indices
equations = {
    "NDVI": "(B[4] - B[3]) / (B[4] + B[3])",
    "NDRE": "(B[4] - B[2]) / (B[4] + B[2])",
    "SAVI": "1.5 * (B[4] - B[3]) / (B[4] + B[3] + 0.5)"
}

# Process multiple indices
index_images = list(simage.calculate_index(equations))

for idx_img in index_images:
    print(f"Index: {idx_img.get_band_alias()}")
    stats = idx_img.calculate_band_stats()
    print(f"Statistics: {stats}")

Image Rendering and Visualization

# Render index with custom colormap
rendered = (index_images[0]  # NDVI
    .render_index(vmin=0, vmax=1, cmap="viridis")
    .rescale(0.5)  # Reduce resolution by 50%
    .to_png("ndvi_visualization.png")
)

Image Merging and Analysis

# Merge multiple index images
merged = SatImage().merge(index_images)

# Get image boundary
boundary = merged.get_boundary()

# Calculate comprehensive statistics
stats = merged.calculate_band_stats()

🏗️ API Reference

Core Class

SatImage

The main class providing a fluent interface for satellite image processing.

Key Methods:

• I/O Operations

  • read_tif(path): Load GeoTIFF files
  • to_tif(path): Save as GeoTIFF
  • to_png(path): Save as PNG

• Data Manipulation

  • change_pixel_dtype(dtype): Convert pixel data type
  • change_nodata(new_nodata, old_nodata): Update nodata values
  • reproject(crs): Reproject to different coordinate system
  • rescale(factor): Resize image by scaling factor
  • shrink(distance): Reduce image extent by specified distance

• Band Operations

  • set_band_alias(aliases): Assign names to bands
  • extract_band(bands): Select specific bands
  • apply_scale_factor(factors): Apply scaling factors to bands
  • calculate_index(equations): Compute spectral indices

• Analysis

  • calculate_band_stats(): Compute statistical metrics
  • get_boundary(): Extract image boundary geometry

• Visualization

  • render_index(vmin, vmax, cmap): Render with color mapping

• Utilities

  • copy(): Create deep copy
  • merge(images): Combine multiple images
  • is_empty(): Check if image data exists

🔧 Dependencies

• numpy (>=1.21.0): Numerical computing
• rioxarray (>=0.13.0): Rasterio integration with xarray
• geopandas (>=0.12.0): Geospatial data handling
• Pillow (>=9.0.0): Image processing
• matplotlib (>=3.5.0): Plotting and visualization
• typeguard (>=4.0.0): Runtime type checking

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

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

📄 License

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

🙏 Acknowledgments

• Built on top of the excellent rasterio and xarray libraries
• Inspired by modern geospatial processing workflows
• Thanks to the open source geospatial community

📞 Support

If you encounter any issues or have questions, please open an issue on GitHub.