Displays the dataset where the image used for generating tie points is located. Not editable.

Based on existing adjustment file information, align new tie points close to the accuracy of existing tie points. Use the Add and Delete buttons on the toolbar to conveniently manage multiple reference adjustment files.

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

The error threshold for eliminating gross errors in image matching. The value range is [0, 40], with a default of 5 (unit: px).

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

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

• Conventional: Divide each overlapping region into N*M sub-regions, then select n image patches of 512×512 size from each sub-region for homologous point matching, ensuring stable and reliable tie points. The generated tie points will cover the entire overlapping area as much as possible.
• Uniform: The generated tie points will be evenly distributed in the overlapping area. The number of points is less than that of the conventional distribution, but the distribution is more uniform, suitable for cases with large internal distortion in the image.

Available when Conventional is selected in Point distribution mode.

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

Available when Conventional is selected in Point distribution mode.

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

Available when Conventional is selected in Point distribution mode.

Six matching methods are provided. Choose based on data characteristics and requirements. Among them, AFHORP and RIFT support multimodal data matching; CASP and DEEPFT are based on deep learning and require additional AI model configuration and CUDA environment installation; generally, MOTIF, CASP, or DEEPFT are recommended.

• MOTIF (default): A template matching algorithm for multimodal images, characterized by lightweight feature descriptors. MOTIF can overcome nonlinear radiation distortions 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. This pipeline decomposes the matching stage into two progressive stages: first establishing one-to-many correspondences at a coarser scale as cascaded priors, then using these priors to guide the determination of one-to-one matches at the target scale.^[1]
• DEEPFT: An image matching method based on deep learning.
• SIFT: A method for extracting distinctive invariant features from images, enabling reliable matching between objects or scenes from different viewpoints.
• RIFT: A feature matching algorithm robust to large-scale nonlinear radiation distortions. It improves the stability of feature detection and overcomes the limitations of gradient-based feature descriptions.
• AFHORP: A feature matching algorithm for multimodal images. AFHORP is highly resistant to radiation distortions and contrast differences in multimodal images, with excellent performance in addressing orientation reversal and phase extremum mutation issues.

Available when Conventional is selected in Point distribution mode.

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

Available when Uniform is selected in Point distribution mode.

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

Available when Uniform is selected in Point distribution mode.

Set the method for searching seed points. The default is Grid center point.

• Corner point: Points with obvious features within the selected region are used as seed points.
• Grid center point: The center point of the grid is used as the seed point. This search method is random.

Available when Uniform is selected in Point distribution mode.

Set the interval size between seed points. The value range is [1, 256], with a default of 40 (unit: px). The larger the template, the more reliable the searched points, but the longer the time taken.

Available when Uniform is selected in Point distribution mode.

Set the search radius for seed point image matching. The value range is [0, 256], with a default of 40 (unit: px). The larger the search radius, the larger the matching range and the longer the time taken.

Displayed after checking cloud area. Not editable.

If the workflow is for DOM production, the Set Image Path cloud amount data is used.

If the workflow is for DSM production, the Set Image Path (DSM/DEM) cloud amount data is used.

Available after checking Semantic culling of non-ground points.

Checked by default, meaning building areas are automatically identified and tie points in those areas are removed. If not checked, tie points in building areas are retained.