Research and exploration on the interpretability of deep learning model in radar image

Abstract

Although deep learning technology has been partially applied in radar image processing, the lack of interpretability analysis and comprehensive performance evaluation of black-box models limits the application performance, credibility, and universality of this technology in the radar image field. Starting from the interpretability, this paper proposes the mechanism analysis idea of deep learning black-box model in the radar image field and carries out experimental verification on the open-source MSTAR radar image dataset. It analyzes the transfer and cognitive mechanisms of the deep learning model and draws significant findings on transfer learning, attribution method application, and model robustness evaluation, filling gaps in the existing literature.