Adaptive image registration for remote sensing

Remote sensing applications require image registration as a pre-processing step before further progress. In this paper, we present a rigid search-space reducing, feature-based adaptive image registration scheme to put images in correspondence, without establishing explicit point correspondences. Our method estimates the registration parameters using a feature set, which is based on Principal Component Analysis (PCA). A unique aspect of the method is the incorporation of a learning process to learn the parameters from a training set of images, which is constructed incrementally. We illustrate the robustness of this approach using a number of remote sensing images and a variety of rotation angles. Mapping between the features and the transformation parameters is via a nearest-mean matching scheme. Hence correct orientation is determined within a predetermined error.