EEKHI–SAR–SIFT: Edge Enhancement and Keypoint Homogeneous Improved SAR–SIFT Framework Based on Unbiased Difference Ratio Edge Detector Strategy

Keypoint matching plays a vital role in the realm of synthetic aperture radar (SAR) image processing, serving as a crucial component within this domain. Another key fact to remember keypoint matching is a crucial step in change detection and image stitching. In the context of SAR images, a scale-invariant feature transformation (SIFT)-based approach, known as SAR–SIFT, presents a notable advantage by mitigating the impact of speckle noise; however, it cannot yield accurate edge information, and the resulting keypoints are nonuniformly distributed. We propose an edge enhancement and homogeneous spatial key point improved SAR–SIFT framework based on an unbiased difference-ratio (UDR) edge detector (called EEKHI–SAR–SIFT) to solve the above problems. The algorithm relies on the characteristics of edge unbiased localization and constant false alarm rate of UDR edge extraction to reduce the extracted wrong corner information as well as enhance the extraction exactness of keypoints. In addition, adaptive nonmaximum suppression (ANMS) method is applied to homogenize the dense keypoints with a large initial number that are gained by means of the EEKHI–SAR–SIFT algorithm and reduce their local clustering. Finally, a descriptor construction strategy that retains multiscale information is adopted to improve the descriptor uniqueness. Tests using multiple sets of SAR image data from different satellites (Gaofen-3, RADARSAT, and Sentinel-1A) demonstrate that the efficacy of the proposed EEKHI–SAR–SIFT algorithm reduces the root mean square error is about 1–2 pixel lower than the final result of the original SAR–SIFT algorithm.