Enhanced Feature Based Mosaicing Technique for Visually and Geometrically Degraded Airborne Synthetic Aperture Radar Images

Abstract

In airborne synthetic aperture radar (SAR), there was a major problem encountered in the area of image mosaic in the absence of platform information and sensor information (geocoding), when SAR is applied in large-scale scene and the platform faces large changes. In order to enhance real-time performance and robustness of image mosaic, enhancement based Speeded-Up Robust Features (SURF) mosaic method for airborne SAR is proposed in this paper. SURF is a novel scale-invariant and rotation-invariant feature. It is perfect in its high computation, speed and robustness. In this paper, When the SAR image is acquired, initially the image is enhanced by using local statistic techniques and SURF is applied for SAR image matching accord to its characteristic, and then acquires its invariant feature for matching. In the process of image matching, the nearest neighbor rule for initial matching is used, and the wrong points of the matches are removed through RANSAC fitting algorithm. The proposed algorithm is implemented in different SAR images with difference in scale change, rotation change and noise. The proposed algorithm is compared with other existing algorithms and the quantitative and qualitative measures are calculated and tabulated. The proposed algorithm is robust to changes and the threshold is varied accordingly to increase the matching rate more than 95 %.