https://github.com/natgluons/geosat-himawari-satellite-data-processing
Resampling and composite image processing for Himawari satellite data (includes resampling, cropping, aggregating, and RGB composite generation).
https://github.com/natgluons/geosat-himawari-satellite-data-processing
Last synced: about 2 months ago
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Resampling and composite image processing for Himawari satellite data (includes resampling, cropping, aggregating, and RGB composite generation).
- Host: GitHub
- URL: https://github.com/natgluons/geosat-himawari-satellite-data-processing
- Owner: natgluons
- Created: 2023-08-16T02:28:37.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2025-03-07T18:28:30.000Z (3 months ago)
- Last Synced: 2025-03-07T19:28:49.203Z (3 months ago)
- Language: Python
- Homepage:
- Size: 10.7 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# **Resampling and Composite Processing for Himawari Satellite Data**
## **Overview**
This project processes **Himawari satellite imagery** using **SatPy** for tasks like resampling, cropping, aggregating, and generating composite images. It enables efficient analysis of meteorological satellite data by unifying resolutions, extracting key features, and visualizing RGB composites.
## **Features**
- **Resampling**: Standardizes image resolution across multiple channels.
- **Cropping**: Extracts specific regions for targeted analysis.
- **Aggregation**: Merges data over time or space for improved clarity.
- **RGB Composite Generation**: Enhances visualization of meteorological data.
- **Automated Image Saving**: Saves processed images as PNG files for further use.
## **Requirements**
- Python
- SatPy
- Cartopy
- Rasterio
- Dask
## **Usage**
1. Mount Google Drive and install dependencies.
2. Load Himawari satellite data using `SatPy`.
3. Perform resampling, cropping, and aggregation.
4. Generate RGB composite images.
5. Save processed images for further analysis.
## **Data Source**
[Himawari Satellite Data (Google Drive)](https://drive.google.com/drive/folders/1oYB7ovRjUQqY9hp3U46OuEHOgWZGPjNj?usp=sharing)
This project provides a streamlined approach for analyzing meteorological satellite images efficiently.