A hybrid feature extraction method for SAR image registration

Extracting and matching correct correspondence between two images are significant stages for feature-based synthetic aperture radar (SAR) image registration. Two methods of feature extraction were employed in this study. Blob features were obtained by combining a Gaussian-guided filter (GGF) with a scale invariant feature transform, and corner features were obtained from the GGF. A GGF can store edge information and operate more effectively than a Gaussian filter. The ratio of average was used to compute gradients in order to reduce the speckle effect. Fast sample consensus (FSC) algorithm was combined with complete graph method for feature correspondence matching. Although FSC algorithm can extract valid correspondence, it may not be efficient enough to deal with SAR images due to its random nature and the large number of outliers in the data. Therefore, a graph-based algorithm was employed to solve the problem by eliminating outliers. The proposed hybrid method was tested on several real SAR images having different properties. The results showed that the proposed method performed the automated registration of SAR images more accurately and efficiently.