Change Detection in Multi-temporal Satellite Images

In this job, I collaborated with Ihebeddine RYAHI

Table of contents

1. Overview
2. Requirements
3. How to detect change?

Overview

In this project, we built a machine learning model to detect changes in multi-temporal satellite images.
It uses Principal Component Analysis (PCA) and K-means clustering techniques over difference image.

Requirements

       • OpenCV (version 4.2.1).
       • Python (version 3.6.9).
       • Scikit-learn ML Library.
       • The directory images contains multi-temporal images developed from the LANDSAT images available in the United States Geological Survey (USGS) website. You can find some of multi-temporal image pairs in images directory.

How to detect change?

       • Go to scripts directory and Run python DetectChange.py -io <FIRST_IMAGE> -it <SECOND_IMAGE> -o <OUTPUT_DIRECTORY> to detect change in two multi-temporal satellite images.
NB: The output directory should end with '/'.
The script will generate a difference image named difference and a ChangeMap image.
Other images are generated depending on morphological transformations.