Generate Ground Control Points

Feature Description

Ground Control Points (GCPs) are control points located at specific positions on imagery with known coordinate information in mapping coordinate systems. Possessing high-precision spatial coordinate data, they are essential for processes such as remote sensing imagery geometric correction, positioning accuracy verification, and spatial registration, enabling high-precision georeferencing and positional tracking of image data.

Supported since SuperMap ImageX Pro 11i(2023) version.

Feature Entry

DOM Automated Processing Flow/DSM Process Window -> Generate Ground Control Points Node.

Parameter Description

Parameter Description Type
Dataset

Displays the dataset containing imagery for GCP generation (read-only).

 DatasetMosaic
Input Image Type Select image type for GCP generation. Default: Panchromatic Image. Other options: Multispectral Image, Forward-Looking Image, Rear-View Image, or Front View and Rear View Image. ComboBoxImageType
Error Threshold Outlier elimination threshold for image matching, range [0,40], default 5 (px). During matching, least squares method filters out points exceeding threshold. Higher values retain more points but increase error probability. Double
Point Distribution Method

Select GCP distribution pattern: Conventional (default) or Uniform.

  • Conventional: Divides overlap areas into N*M subregions, selects n 512x512 image blocks per subregion for tie point matching, ensuring stable high-quality points. Generated GCPs maximally cover overlap areas.
  • Uniform: Generates evenly distributed GCPs across overlap areas. Fewer points than conventional method but more military-standard distribution, suitable for imagery with significant internal distortion.
 PointDistributionMethod
Density

Available when Point Distribution Method is Conventional.

Set GCP generation density: Sparse, Medium (default), or Dense. Higher density requires longer processing time.

 ImageMatchPointDensityLevel
Matching Method

Available when Point Distribution Method is Conventional.

Options: MOTIF (default), AFHORP, RIFT, SIFT, DEEPFT. AFHORP and RIFT support multimodal data matching. DEEPFT requires AI model configuration and CUDA installation.

  • MOTIF: A template matching algorithm for multimodal imagery, characterized by lightweight feature descriptors. MOTIF overcomes nonlinear radiometric distortions caused by differences between SAR and optical images.
  • AFHORP: A feature matching algorithm for multimodal imagery. AFHORP exhibits strong resistance to radiometric distortions and contrast differences in multimodal images, with excellent performance in resolving directional inversions and phase extremum mutations.
  • RIFT: A feature matching algorithm robust to large-scale nonlinear radiometric distortions. RIFT enhances feature detection stability and overcomes limitations of using gradient information for feature description.
  • SIFT: A method for extracting unique invariant features from images, enabling reliable matching of objects or scenes across different viewpoints.
  • DEEPFT: An image matching method based on deep learning.
 ImageMatchMethod
Maximum Points Per Block

Available when Point Distribution Method is Conventional.

Maximum points retained per image block during matching, range [25,2048], default 256.

 Integer
Seed Point Count

Available when Point Distribution Method is Uniform.

Number of seed points for tie point matching per scene, range [64,6400], default 512. Increase for low-texture imagery to ensure sufficient points for subsequent processing.

 Integer
Seed Point Search Method

Available when Point Distribution Method is Uniform.

Select seed point search method: Corner Points (feature points) or Grid Center Points (default, random selection).

 SearchSeedPointMethod
Template Size

Available when Point Distribution Method is Uniform.

Interval between seed points, range [1,256], default 40 (px). Larger templates improve reliability but increase processing time.

 Integer
Search Radius

Available when Point Distribution Method is Uniform.

Seed point search radius for image matching, range [0,256], default 40 (px). Larger radius expands search area but increases processing time.

 Double
Semantic Culling of Non-Ground Points Disabled by default. When enabled, automatically culls GCPs in cloud areas and building areas using AI semantic technology. Boolean
Cloud Area

Available when Semantic Culling of Non-Ground Points is enabled.

Enabled by default. Uses specified dataset to automatically cull GCPs in cloud areas. Disable to retain cloud area GCPs.

 Boolean
Dataset

Displayed when Cloud Area is enabled (read-only).

For DOM Automated Processing Flow: Uses cloud amount data from Set Image Path.

For DSM Process: Uses cloud amount data from Set Image Path (DSM/DEM).

 DatasetVector
Building Area Available when Semantic Culling of Non-Ground Points is enabled. Enabled by default. Automatically identifies and culls GCPs in building areas. Disable to retain building area GCPs. Boolean

Output

Generates GroundControlPoint vector point dataset in Control Point datasource.

Related Topics

Set Image Path

Set Image Path (DSM/DEM)

Generate Tie Points

Block Adjustment