LAUSR

Accurate registration between synthetic aperture radar (SAR) images is the basis for high-precision geometric correction of SAR images. The feature points extracted by conventional feature extraction methods are unsatisfactory, which are affected by imaging geometric characteristics and speckle noise of SAR images. This article innovatively proposes a spaceborne SAR image feature learning framework to realize automatic sample generation and model training. It mainly includes two modules: The feature sample generation module based on the initial geometric information of spaceborne SAR. The initial rational polynomial coefficient (RPC) parameters of the spaceborne SAR are adopted to realize the initial positioning of the SAR image, and a variety of feature extraction operators are used to match the overlapping areas to obtain high-precision matching points, which are employed as training samples for image pairs; pseudo-Siamese feature learning network SARPointNet for SAR image feature learning. The pseudo-Siamese network is used to extract the feature points and descriptors of the sample image pairs. The feature optimization process is realized through the descriptor constraints between image pairs, which promotes the network to improve the accuracy of feature extraction. The proposed method has been tested in mountain, hilly, flatland, and urban scenarios, respectively. The results demonstrate that the correspondence points extracted by SARPointNet are evenly distributed, are in large quantities (at least ten times that of other methods), and achieve high precision (the root mean square error is less than 1 pixels), which shows great advantages over other methods.