SAR Image Registration with Optimized Feature Descriptor and Reliable Feature Matching

Abstract

The scale-invariant feature transform (SIFT) algorithm has been widely used in feature-based remote sensing image registration. However, it may be difficult to find sufficient correct matches for SAR image pairs in some cases that exhibit significant intensity difference and geometric distortion. In this letter, a new robust feature descriptor extracted with Sobel operator and improved gradient location orientation hologram (GLOH) feature is introduced to overcome nonlinear difference of image intensity between SAR images. Then, an effective false correspondences removal method by improving the analysis of bivariate histogram is used to refine the initial matches. Finally, a reliable method for affine transformation error analysis of adjacent features is put forward to increase the number of correct matches. The experimental results demonstrate that the proposed method provides better registration performance compared with the standard SIFT algorithm and SAR-SIFT algorithm in terms of number of correct matches, correct match rate and aligning accuracy.