Combining Optimized SAR-SIFT Features and RD Model for Multisource SAR Image Registration

Multisource synthetic aperture radar (SAR) image registration is a difficult task in remote sensing due to the influence of speckle noise and geometric distortions between the images. The SAR scale-invariant feature transform (SAR-SIFT) is a prior option to extract features for automatic registration of SAR images. However, most registration methods based on the SAR-SIFT operator only use the image information (the geometric distribution of the pixels making up the feature) and do not consider the influences of the side-looking imagery mode and the topographic relief. In this article, we propose a novel registration method to address the above problems, which combines the optimized SAR-SIFT features and the imaging geometry of the SAR system. The major improvement of the algorithm is the use of the range-Doppler (RD) model in feature matching and geometric transformation estimation. A new local matching method aided by the RD model, transforming the global matching mode to the local one, is first introduced to avoid the gross errors and improve the matching efficiency. Then, an RD-model-based geometric transformation model is presented for multisource SAR images with obvious geometric distortions. In addition, the stationary wavelet transform (SWT) is brought in the feature extraction to optimize the SAR-SIFT keypoints to extract reliable and uniformed features. The experimental results fully demonstrate the applicability of the proposed method for registration of multisource SAR images in different acquiring configurations, especially with different passes.