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The implementation of automatic image registration is still difficult invarious applications. In this paper, an automatic image registration approachthrough line-support region segmentation and geometrical outlier removal(ALRS-GOR) is proposed. This new approach is designed to address the problemsassociated with the registration of images with affine deformations andinconsistent content, such as remote sensing images with different spectralcontent or noise interference, or map images with inconsistent annotations. Tobegin with, line-support regions, namely a straight region whose points shareroughly the same image gradient angle, are extracted to address the issues ofinconsistent content existing in images. To alleviate the incompleteness ofline segments, an iterative strategy with multi-resolution is employed topreserve global structures that are masked at full resolution by image detailsor noise. Then, Geometrical Outlier Removal (GOR) is developed to providereliable feature point matching, which is based on affineinvariant geometricalclassifications for corresponding matches initialized by SIFT. The candidateoutliers are selected by comparing the disparity of accumulated classificationsamong all matches, instead of conventional methods which only rely on localgeometrical relations. Various image sets have been considered in this paperfor the evaluation of the proposed approach, including aerial images withsimulated affine deformations, remote sensing optical and synthetic apertureradar images taken at different situations (multispectral, multisensor, andmultitemporal), and map images with inconsistent annotations. Experimentalresults demonstrate the superior performance of the proposed method over theexisting approaches for the whole data set.

Automatic Registration of Images with Inconsistent Content Through Line-Support Region Segmentation and Geometrical Outlier Removal