Abstract
The performance of affine invariant feature detector-Hessain Affine on Terra SAR-X images decreases in the presence of speckle noise due to false feature point detections and matches. The owing inherent characteristics of Terra SAR-X images like broad dynamic range and the multiplicative nature of speckle noise, the gradient magnitude is strengthened on homogeneous regions. Hence the false feature point detections on high contrast regions are not suppressed. In order to make feature detection robust towards the speckle noise, the use of improved version of Grunwald-Letnikov (G-L) fractional differential operator in the development of feature detection is investigated. This paved the way for a new affine invariant fractional order feature detector. This detector allows a stable and fine feature point selection without fine tuning parameters of the Hessian Affine feature detector. The algorithm proposed is tested on TerraSAR-X (TSX) images with different look angles. The results show that the proposed Fractional Affine algorithm improves the feature matching performance compared to Hessian Affine algorithm in terms of number of true correspondences and registration accuracy.
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