Automated designation of tie-points for image-to-image coregistration

Abstract

Image-to-image registration requires identification of common points in both images (image tie-points; ITPs). Here, we describe software implementing an automated, area-based technique for identifying ITPs. The ITP software was designed to follow two strategies: (1) capitalize on human knowledge and pattern-recognition strengths, and (2) favour robustness in many situations over optimal performance in any one situation. We tested the software under several confounding conditions, representing image distortions, inaccurate user input, and changes between images. The software was robust to additive noise, moderate change between images, low levels of image geometric distortion, undocumented rotation, and inaccurate pixel size designation. At higher levels of image geometric distortion, the software was less robust, but such conditions are often visible to the eye. Methods are discussed for adjusting parameters in the ITP software to reduce error under such conditions. For more than 1600 tests, median time needed to identify each ITP was approximately 8 s on a common image-processing computer system. Although correlation-based techniques--such as those implemented in the free software documented here--have been criticized, we suggest that they may, in fact, be quite useful in many situations for users in the remote sensing community.