Techniques for Change Detection in Satellite Images

1. Image Differencing:

• Definition: Subtract pixel values of one image from another taken at different times.
• Process:
  • Align images using geometric correction.
  • Calculate difference for each pixel.
  • Apply threshold to identify significant changes.
• Use Case: Quick and simple change detection like disaster impact assessment.

2. Image Ratioing:

• Definition: Divide pixel values of one image by the corresponding pixel values of another.
• Process:
  • Ratio each pixel value from one image with the corresponding pixel value from the other.
  • Highlight areas with significant ratio differences.
• Use Case: Detect subtle changes in vegetation or water bodies.

3. Change Vector Analysis (CVA):

• Definition: Measure the magnitude and direction of changes in pixel vectors in a multi-spectral space.
• Process:
  • Calculate the change vector for each pixel.
  • Analyze the magnitude and direction of these vectors.
• Use Case: Detailed analysis of multi-spectral changes, such as land cover change.

4. Principal Component Analysis (PCA):

• Definition: Reduce dimensionality of data and highlight change by transforming the data to principal components.
• Process:
  • Combine multi-temporal images into a single dataset.
  • Apply PCA to transform the data.
  • Identify changes using principal components.
• Use Case: Extract significant changes from noisy data.

5. Post-classification Comparison:

• Definition: Classify each image independently and then compare the classifications.
• Process:
  • Classify each image into different land cover classes.
  • Compare classifications to identify changes.
• Use Case: Detailed land use and land cover change detection.

6. Multi-temporal Composite Analysis:

• Definition: Combine multiple images over time to detect changes.
• Process:
  • Create composite images from multiple time periods.
  • Analyze composites for changes over time.
• Use Case: Seasonal changes and long-term environmental monitoring.

7. Machine Learning Techniques:

• Random Forest, Support Vector Machine (SVM):
  • Train classifiers on labeled change and no-change areas.
  • Predict changes on new images.
• Deep Learning (CNN, RNN):
  • Use deep learning models to learn complex patterns and changes.
  • Apply models to detect changes automatically.
• Use Case: High-accuracy change detection in complex environments.

8. Normalized Difference Vegetation Index (NDVI) Differencing:

• Definition: Use NDVI to monitor vegetation changes.
• Process:
  • Calculate NDVI for images from different times.
  • Subtract NDVI values to identify changes in vegetation.
• Use Case: Agricultural monitoring and forest cover changes.

9. Object-based Change Detection:

• Definition: Detect changes based on image objects rather than individual pixels.
• Process:
  • Segment images into meaningful objects.
  • Compare objects over time for changes.
• Use Case: Urban development and infrastructure changes.