A Scalable Probabilistic Change Detection Algorithm for Very High Resolution (VHR) Satellite Imagery
Detecting landscape changes using very high- resolution multispectral imagery demands an accurate and scalable algorithm that is robust to geometric and atmospheric errors. Existing pixel-based change detection approaches, how- ever, have several drawbacks, which render them ineffective for VHR imagery analysis. This is an implementation of a probabilistic change detection framework provides more accurate assessment of changes than traditional approaches by analyzing image patches than pixels.
These instructions will get you to set the project up and running on your local machine for development and testing purposes. Place both the satellite images in the same directory as the Spatial.py file and adjust the indices in the code to your preference.
python Spatial.py
- Shivaprakash Balasubramanian (sbalas22)
- Meghana Ravindra Vasist (mravind)
This project is licensed under the MIT License.