In this paper, based on the framework of the Radiation Invariant Feature Transform (RIFT), we make innovative improvements from the perspective of feature computation, and then propose a multi-view radar image alignment method that integrates multi-feature description and shadow perception. In the prior art, most of the radar image alignment methods tend to rely on a single feature for description, which makes them show obvious limitations when facing complex nonlinear radial aberrations and multi-view changes. The method proposed in this paper, on the other hand, skillfully utilizes the spatial domain qualities of the real and imaginary parts of the log-Gabor filter in the specific stage of feature computation to accurately extract two types of significantly different features in the radar image. This multi-feature description breaks the constraints of the traditional single feature and is able to capture the information in the image in a more comprehensive and detailed way. In addition, the introduction of shadow perception mechanism is another major innovation of this method. By effectively identifying and analyzing the shadow region, the feature description is further supplemented and improved, thus making the alignment process more accurate and reliable. Shadow regions usually contain important structural and depth information, which is often neglected or mishandled by existing techniques. The present method, however, is able to fully utilize such information and effectively avoid alignment errors caused by shadows. This innovative initiative not only effectively avoids the adverse effects of single feature changes on the characterization, but also greatly improves the robust performance of the algorithm to cope with nonlinear radial aberrations. After a series of rigorous experimental validation, the method proposed in this paper shows significantly better performance than the existing algorithms in terms of root-mean-square error, probability of successful matching, and the number of correctly matched points, successfully realizing the high-precision alignment of radar images under multiple viewpoints. It provides valuable theoretical reference and practice for the development of related fields, and contributes to the progress of radar image alignment technology.