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Abstract Image registration plays an important role in the
fields such as change detection, establishing the geographical
information system, medical image and military detection. In
general, the approach to image registration consists of four
steps: feature-points selection, feature-points pairing, mapping
function estimation, and image registration. The most difficult
parts are to select and pair feature-points in different images.
We extract the centroids of the most conspicuous parts in each
image as feature-points. The method we adopt is to analyze the
distribution of all feature-points in each image to get the
principle axes by Principle Components Analysis. Then we
normalize the distribution of all the points and obtain their
normalized coordinates according to the new axes in each image.
Scale, rotation and translation variations between two images
can be removed by the normalization process. The influence of
spurious and missing points can be alleviated by the outliers
discard strategy. In this thesis, experimental results of
simulation data and satellite images are presented. It is shown
that the satellite images are overlaid well by the method we
adopt even though the overlapping regions are not very large.
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