A new similarity measure based on Hausdorff Distance Matrix Frobenius Norm for object matching is proposed in this
paper. This measure is more reliable and can achieve higher location accuracy compared with other measures based on
classic and modified Hausdorff Distance under the condition of high level noise and high ratio occlusion of template.
The search strategy based on genetic algorithms is employed to make algorithm faster. Experimental results under noise
of different level demonstrate high performance of the matching algorithm.
A novel algorithm for matching synthetic aperture radar (SAR) image to Optical image based on lineal feature using
Hausdorff distance combined with genetic algorithm is proposed in this paper. A new method is presented to extract
lineal feature from low signal to noise ratio (SNR) SAR image. Based on the edge image from SAR and Optical image,
modified Hausdorff distance is adopted as a similarity measure because it is insensitive to noise. Genetic algorithm is
used as searching strategy to achieve high computation speed for its inertial parallel. Experimental results using real SAR
and Optical images demonstrate that the algorithm is robust, fast and can achieve high matching accuracy.