Displays the dataset containing the imagery used for generating connection points. It is not editable.

Based on existing adjustment file information, make the newly generated connection points approximate the accuracy of the existing ones. Use the Add and Delete buttons in the toolbar to conveniently manage multiple reference adjustment files.

The reference adjustment file is obtained from the Block Network Adjustment function.

The error threshold for gross error elimination during image matching. The value range is [0,40]. The default is 5, and the unit is px.

During the image matching process, the least squares method is used to fit the result points, removing points greater than the error threshold. A larger threshold retains more connection points but increases the probability of retaining erroneous points.

Select the connection point distribution mode. Two methods are provided: Conventional and Uniform. The default is Conventional.

• Conventional: Divides each overlapping area into N*M sub-regions, then selects n 512*512 image blocks from each sub-region for homologous point matching, ensuring the stability and reliability of the homologous points. The generated connection points will cover the entire overlapping area as much as possible.
• Uniform: The generated connection points will be evenly distributed within the overlapping area. The number of points is less than in the conventional distribution, but the distribution is more uniform. This is suitable for situations where the internal distortion of the imagery is significant.

Available when Conventional is selected in Point Distribution Mode.

The number of blocks into which each overlapping area is divided in the column direction. The default value is 4.

Available when Conventional is selected in Point Distribution Mode.

The number of blocks into which each overlapping area is divided in the row direction. The default value is 4.

Available when Conventional is selected in Point Distribution Mode.

Six matching methods are provided, and one can be selected based on data characteristics and requirements. Among them, the AFHORP and RIFT methods support multimodal data matching; CASP and DEEPFT are based on deep learning and require additional configuration of AI models and installation of the CUDA environment. In general, it is recommended to use MOTIF, CASP, or DEEPFT.

• MOTIF (default): A template matching algorithm for multimodal imagery, characterized by the use of lightweight feature descriptors. MOTIF can overcome nonlinear radiometric distortion caused by differences between SAR and optical images.
• CASP: A novel cascaded matching pipeline that benefits from integrating high-level features, helping to reduce the computational cost of low-level feature extraction. The pipeline decomposes the matching phase into two progressive stages, first establishing one-to-many correspondences at a coarser scale as cascaded priors. Then, using these priors for guidance, one-to-one matches are determined at the target scale.^[1]
• DEEPFT: An image matching method based on deep learning.
• SIFT: A method for extracting distinctive invariant features from images, which can be used for reliable matching between objects or scenes under different viewing angles.
• RIFT: A feature matching algorithm robust to large-scale nonlinear radiometric distortion. It can improve the stability of feature detection and overcome the limitations of feature description based on gradient information.
• AFHORP: A feature matching algorithm for multimodal imagery. AFHORP has strong resistance to radiometric distortion and contrast differences in multimodal imagery and performs excellently in solving problems of orientation reversal and phase extremum mutation.

Available when Conventional is selected in Point Distribution Mode.

The maximum number of points retained within each image block during image matching. The value range is [1,2048]. The default is 256.

Available when Uniform is selected in Point Distribution Mode.

Sets the number of seed points for homologous point matching on each image scene. The value range is [64,6400]. The default is 512. When the image texture is poor, it is recommended to increase the number of connection points to match enough points and improve subsequent imagery quality.

Available when Uniform is selected in Point Distribution Mode.

Sets the method for searching seed points. The default is Raster Center Point.

• Corner Point: Select points with distinct features within the region as seed points.
• Raster Center Point: Use the center point of the raster as the seed point. This search method is random.

Available when Uniform is selected in Point Distribution Mode.

Sets the interval size between seed points. The value range is [1,256]. The default is 40, and the unit is px. A larger template makes the searched points more reliable but takes longer.

Available when Uniform is selected in Point Distribution Mode.

Sets the search radius for seed points during image matching. The value range is [0,256]. The default value is 40, and the unit is px. A larger search radius increases the matching range and also the processing time.

Displayed after Cloud Area is selected. It is not editable.

For workflows related to DOM production, use the cloud amount data from Set the Image Path.

For workflows related to DSM production, use the cloud amount data from Set Image Path (dsm/dem).

Available after selecting Semantic Culling of Non-ground Points.

Selected by default, meaning building areas will be automatically identified and connection points within those areas will be culled. If not selected, connection points within building areas will be retained.