Image to Points
Matches image locations to known control/check locations.
Best used when: reliable ground/control coordinates are available.
Raster Rectification
Geometrically align an image using matched control points. Reference points can come from known ground points, another image, or a basemap.
Accuracy & RectificationEnd-User DocumentationTheory + Formula + Parameters1. What This Feature Does
Geometrically align an image using matched control points. Reference points can come from known ground points, another image, or a basemap.
Cloud workflow: choose data → configure the scientific method → run → review the resulting layer/report. No programming is required.
2. Recommended Workflow
Choose Image to Points, Image to Image, or Image to Basemap.
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Create at least the required control-point pairs distributed across the image.
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Inspect residual/geometric consistency visually.
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Choose output resampling.
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Run rectification and verify against independent features.
3. Theory, Methods & Equations
Image to Image
Matches identifiable features between the source raster and a reference image.
Best used when: a trusted georeferenced image is the best reference.
Image to Basemap
Matches identifiable features to a basemap.
Best used when: quick visual alignment to an existing map reference is required.
Geometric Warp
A fitted coordinate transformation maps source coordinates to reference coordinates. With a simple affine model,
x′=a0+a1x+a2y
y′=b0+b1x+b2y
Best used when: control points support a stable transformation.
4. Input Data
| Input | Type | Requirement | Description |
|---|---|---|---|
| Source Raster | raster | Required | Raster to rectify. The original raster is never overwritten. |
5. Parameters Available in the Application
| Parameter | Default | Choices / Range | How to Use It |
|---|---|---|---|
| Reference Method | — | Image to Points, Image to Image, Image to Basemap | |
| GCP / Control Point Pairs | — | — | Browser-generated coordinate pairs. Every item contains source_lon/source_lat and reference_lon/reference_lat in EPSG:4326. |
| Resampling | bilinear | Nearest (classes/discrete data), Bilinear (continuous imagery), Cubic (continuous imagery) | |
| Output EPSG | 4326 | 1 to — | iTSensing Cloud Raster Rectify output is currently EPSG:4326. |
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.
Important: Control points should be well distributed rather than concentrated in one corner. Nearest resampling preserves class values; Bilinear/Cubic suit continuous imagery.
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.