Displays the original multispectral image data used for registration, and it is not editable.

Available after checking Use ortho panchromatic data.

Displays the dataset containing the panchromatic image data after orthorectification, and it is not editable.

Six matching methods are provided, and can be selected based on data characteristics and requirements. Among them, the AFHORP and RIFT methods support multi-modal 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 multi-modal images, characterized by the use of lightweight feature descriptors. MOTIF can overcome nonlinear radiation distortion caused by differences between SAR and optical images.
• CASP: A novel cascade matching flow, which benefits from integrating high-level features, helping to reduce the computational cost of low-level feature extraction. This flow decomposes the matching stage into two progressive stages, first establishing one-to-many correspondences on a coarser scale as cascade priors. Then, using these priors for guidance, one-to-one matches are determined on 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 from different viewpoints.
• RIFT: A feature matching algorithm robust to large-scale nonlinear radiation 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 multi-modal images. AFHORP has strong resistance to radiation distortion and contrast differences in multi-modal images and performs excellently in solving problems of orientation reversal and abrupt phase extremum changes.

Provides Linear RPC Correction Model, Quadratic Polynomial RPC Correction Model, and Cubic Polynomial RPC Correction Model. Linear RPC Correction Model is selected by default.

• Linear RPC Correction Model: Suitable for cases where internal distortion of the image is small. This model requires no less than 4 ground control points. Please add control point information via the Manage Ground Control Points function.
• Quadratic Polynomial RPC Correction Model: Suitable for cases where internal distortion of the image is moderate. This model requires no less than 9 ground control points. Please add control point information via the Manage Ground Control Points function.
• Cubic Polynomial RPC Correction Model: Suitable for cases where internal distortion of the image is large. This model requires no less than 16 ground control points. Please add control point information via the Manage Ground Control Points function.

If executing the production workflow, the Correction model parameter in Block Network Adjustment will be used, and it is not editable.

Specifies the method for setting the output resulting image resolution. Original resolution is selected by default.

• Original resolution: Retains the original resolution of the input image.
• Fixed Value: Uses the input fixed value as the output image resolution.
• Custom: Outputs different resolutions based on different satellite sensor types. After selecting the Custom option, the Custom Resolution: dialog box will pop up. You can click the Add button on the toolbar to add a record. Then click the cell in the Satellite sensor type column to select the sensor type. Finally, input the resolution corresponding to the sensor type in the cell of the Resolution column.
  Notes:

  • If the corresponding satellite sensor type is not found, please select the USEDEFINE option. After selecting this option, the system will use the resolution corresponding to USEDEFINE to output all data of unsupported satellite sensor types.
  • If processing image data of multiple satellite sensor types simultaneously (e.g., ZY3, GF-2, LandSat) but only set the resolution for one type (e.g., only set for ZY3), images of other satellite sensor types (e.g., GF-2 and LandSat) will be output using their original resolution.

If executing the production workflow, the Resolution type parameter in Generate Orthophoto will be used. When the resolution type in Generate Orthophoto is Fixed Value or Custom, the value here will be automatically calculated as 4 times.