Change Detection

Change detection is the process of finding differences on the Earth's surface over time by comparing satellite images of the same area taken on different dates.

After supervised classification, two classified maps (e.g., Year-1 and Year-2) are compared to identify land use / land cover changes.

Goal

To detect where, what, and how much change has occurred
To monitor urban growth, deforestation, floods, agriculture, etc.

Basic Concept

Forest → Forest = No change
Forest → Urban = Change detected

Key Terminologies

Multi-temporal images: Images of the same area at different times
Post-classification comparison: Comparing two classified maps
Change matrix: Table showing class-to-class change
Change / No-change: Whether land cover remains same or different

Main Methods

Post-classification comparison – Most common and easy
Image differencing – Subtract pixel values
Image ratioing – Divide pixel values
Deep learning methods – Advanced AI-based detection

Examples

Agriculture → Urban (city expansion)
Forest → Barren (deforestation)
Land → Water (flooding)

Change detection is the identification and analysis of land use or land cover changes by comparing classified satellite images of the same area taken at different times.

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