SAR Image Matching Based on Speeded Up Robust Feature

Speeded-Up Robust Features (SURF) is a novel scale-invariant and rotation-invariant feature. It is perfect in its high computation speed and robustness. In this paper, we apply SURF in SAR image matching accord to its characteristic, and then acquire its invariant feature for matching in an addition of no any pre-processing. In the process of image matching, we use the nearest neighbor rule for initial matching, where after, remove the wrong points of the matches through RANSAC. All this method was called R-SURF(RANSAC-SURF). In this method, the threshold range of the nearest neighbor rule has been obtained with our experiment. Experimental results indicated that the threshold interval was [0.6~0.7], and the threshold that you choose in this interval, increased little matching time, but got more than 95% correct matching rate. We used three different types of SAR images in experiments which are in order to put to the proof that SURF is more robust in scale change, rotation change and noise.