Introduction to Change Detection

Change Detection Basics:

• Definition: Identifying changes in land cover, vegetation, urbanization, etc., over time using satellite images.
• Purpose: Monitor environmental changes, urban development, deforestation, disaster assessment, etc.

Types of Change Detection:

1. Bi-temporal Change Detection: Comparing two images from different times.
   • Example: Pre- and post-disaster images.
2. Multi-temporal Change Detection: Analyzing multiple images over time.
   • Example: Seasonal changes in vegetation.

Key Steps in Change Detection:

1. Image Acquisition: Obtain satellite images from sources like Sentinel-1, Sentinel-2, or Landsat.
2. Preprocessing:
   • Radiometric Correction: Adjust for sensor and atmospheric distortions.
   • Geometric Correction: Align images to a common coordinate system.
3. Image Differencing: Subtract pixel values of one image from another.
   • Thresholding: Define a threshold to classify changes.
4. Post-classification Comparison: Classify each image independently and compare the results.
5. Machine Learning Approaches: Use algorithms like Random Forest, SVM, or deep learning models for more complex change detection tasks.

Challenges:

• Noise and Disturbances: Cloud cover, shadows, and seasonal variations can affect accuracy.
• Data Availability: Frequent and high-resolution images are needed for detailed analysis.

Applications:

• Environmental Monitoring: Track changes in forests, wetlands, and water bodies.
• Urban Planning: Observe urban expansion and infrastructure development.
• Disaster Management: Assess impact and recovery after natural disasters like floods or earthquakes.
• Agriculture: Monitor crop health, growth stages, and land use changes.

Tools and Techniques:

• Remote Sensing Software: Use tools like Google Earth Engine, QGIS, or ArcGIS.
• Deep Learning Models: Apply advanced CNNs, RNNs, or transformers for precise change detection.