A Target Recognition Algorithm Based on Multi-Incidence Angle SAR Images

Currently, many target classification and recognition methods for synthetic aperture radar (SAR) images rely on extracting common features of targets from images at different azimuth angles. These methods are highly dependent on the quantity of target image data and are sensitive to feature variations. Specifically, when changes in incidence angle cause image differences, the recognition accuracy of such methods fails to obtain satisfying results. To address the above issues, a solution based on simulating human visual characteristics is proposed to facilitate feature interaction between images taken from different incident angles, thereby identifying the variation patterns of target features. By integrating convolutional gated recurrent units, weighted coupling units, and capsule networks, a target recognition network is designed that takes multi-incident angle images as input. Additionally, to enhance the correlation between consecutive results and improve recognition accuracy, a decision attention mechanism (DAM) is introduced that optimizes multistep classification decisions. The output capsule vectors from multiple passes through the network are enhanced in a specialized reinforcement manner to strengthen classification features and correct misclassification issues during the decision-making process. The experiments are conducted on simulated multi-incidence angle vehicle and ship targets, followed by fine-tuning experiments on a small set of real target samples using the model trained on simulated data. The results demonstrate that the proposed method exhibits superiority and robustness on both simulated and real samples.