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.


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    Title :

    Adaptive image registration for remote sensing


    Contributors:
    Gokcen, I. (author) / Pineda, I.H. (author) / Buckles, B.P. (author)


    Publication date :

    2003-01-01


    Size :

    473849 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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