Automated Image Registration Using Geometrically Invariant Parameter Space Clustering (gipsc)

Accurate, robust, and automatic image registration is a critical task in many typical applications that employ multi-sensor and/or multi-date imagery information. In this paper we present a new approach to automatic image registration, which obviates the need for feature matching and solves for the registration parameters in a Hough-like approach. The basic idea underpinning GIPSC methodology is to pair each data element belonging to two overlapping images, with all other data in each image, through a mathematical transformation. The results of pairing are encoded and exploited in histogram-like arrays as clusters of votes. Geometrically invariant features are adopted in this approach to reduce the computational complexity generated by the high dimensionality of the mathematical transformation. In this way, the problem of image registration is characterized, not by spatial or radiometric properties, but by the mathematical transformation that describes the geometrical relationship between the two images or more. While this approach does not require feature matching, it does permit recovery of matched features (e.g., points) as a useful by-product. The developed methodology incorporates uncertainty modeling using a least squares solution. Successful and promising experimental results of multi-date automatic image registration are reported in this paper.