Difference (After − Before)
Preserves the sign and magnitude of change.
Δ = Xafter − Xbefore
Best used when: increase versus decrease has physical meaning.
Raster Change Detection
Compare two aligned rasters from different dates and produce a continuous or classified change map.
Remote Sensing CoreEnd-User DocumentationTheory + Formula + Parameters1. What This Feature Does
Compare two aligned rasters from different dates and produce a continuous or classified change map.
Cloud workflow: choose data → configure the scientific method → run → review the resulting layer/report. No programming is required.
2. Recommended Workflow
Choose Before and After rasters/bands.
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Ensure comparable radiometric meaning and align grids.
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Choose Difference, Absolute Difference, Percent Change, or Threshold Classification.
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Set thresholds where required.
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Interpret changes in relation to sensor noise and seasonal variation.
3. Theory, Methods & Equations
Absolute Difference
Reports magnitude without direction.
|Δ| = |Xafter − Xbefore|
Best used when: any large departure matters regardless of direction.
Percent Change
Normalizes change relative to the before value.
%Δ = 100 × (After−Before)/Before
Best used when: relative rather than absolute change is needed; near-zero baselines require caution.
Threshold Classification
Converts a difference measure into change/no-change or change classes using analyst-defined thresholds.
Class = f(Δ,T1,T2,…)
Best used when: a discrete operational change mask is required.
4. Input Data
| Input | Type | Requirement | Description |
|---|---|---|---|
| Before Raster | raster | Required | |
| After Raster | raster | Required |
5. Parameters Available in the Application
| Parameter | Default | Choices / Range | How to Use It |
|---|---|---|---|
| Before Band | — | — | |
| After Band | — | — | |
| Change Method | difference | Difference (After - Before), Absolute Difference, Percent Change, Threshold Classification | |
| Change Threshold | 0 | 0 to — | Shown for: threshold_class |
| Alignment Resampling | bilinear | nearest, bilinear, cubic |
6. Output & Interpretation
The result should be interpreted according to the selected method and the physical meaning of the input data. Preserve source units, coordinate reference information, NoData meaning, acquisition date, and preprocessing level when comparing results.
Scientific interpretation: an algorithm can produce a numerically valid result even when the input data are unsuitable. Always check masks, units, sensor characteristics, spatial resolution and reference data.
7. Best Practices
- Use analysis-ready inputs and remove invalid/cloud/noise artifacts that are not part of the target phenomenon.
- Choose parameters from the physical scale of the data, not only from visual appearance.
- Keep categorical and continuous rasters conceptually separate when selecting interpolation/resampling methods.
- Compare the result with the source image and independent reference information.
- Document the settings used when results will be compared across dates, sensors or study areas.