Collecting tie points automatically

Used in rigorous models, such as Aerial Photography, Optical Satellite Modelling, and Radar Satellite Modelling math models, tie points (TP) identify how the images in your project relate to each other. You can collect TPs and ground control points (GCP) to compute a transformation to improve the fit between the images. Because TPs are simply matching points in two or more images, OrthoEngine can automate the collection of TPs by using image-correlation techniques.

Typically, when the two images being matched have similar gray values and appearance, Fast Fourier Transform Phase (FFTP) produces acceptable results. When there is a scale error in the initial math models, Normalized Cross-Correlation (NCC) may produce better matching results than FFTP.

An enhanced option is Fast Fourier Transform Phase with Blunder Detection (FFTPB), which uses FFTP matching as its core, but utilizes a blunder detection algorithm to eliminate poorly matched points prior to bundle adjustment.

When images do not contain a math model, or have incorrect orientation (rotations), you can use feature-based matching (FBM) to create a initial model, which you can then use with FFTP, FFTPB or NCC. It is highly recommended to use FFTPB after FBM. FBM is only available for aerial projects.

When working with unmanned aerial vehicle (UAV) data, it is highly recommended to use the Two Pass: FBM and FFTPB method. This method was designed with UAV data in mind, and fully automates point collection and refinement, as well as camera self-calibration if required. Two Pass: FBM and FFTPB is only available for aerial projects.

When working with SAR data, FFTP matching is recommended.

To collect tie points automatically