Smooth Distribution and Depth-Focused Distillation-Based Class-Incremental Learning for SAR Target Detection

Abstract

Incremental learning methods can overcome the problem of catastrophic forgetting in synthetic aperture radar (SAR) target detection models when continuously learning new class data. However, due to the characteristics of SAR images, such as large background variations and relatively stable scale differences between different target classes, existing advanced incremental detection methods perform poorly in the SAR domain. Specifically, current incremental detection research lacks adaptability to spatial distribution changes and insufficiently focuses on the model’s localization knowledge, making it significantly limited when addressing the aforementioned SAR characteristics. To tackle these issues, we propose a class-incremental learning method for SAR target detection based on smooth distribution and depth-focused knowledge distillation (SDDFD). First, we design a spatial dimension-based smooth distribution distillation (SDDL) method, which evaluates the effectiveness of spatial response points through concentration assessment and dynamically assigns weights to adapt to different background spatial distributions. Then, based on SDDL, we propose an embeddable depth-focused distillation (DFDL) approach. This approach innovatively enhances shallow localization knowledge and deep classification knowledge from a depth perspective, significantly improving the model’s localization ability. Experimental results show that our method outperforms advanced incremental detection methods in various incremental task scenarios, achieving optimal performance. For example, in one-step incremental tasks, the AP0.5 of SDDFD compared to the state-of-the-art method ERD, improved by 8.8% and 4.0% on the MSAR-1.0 and SAR-AIRcraft-1.0 datasets, respectively.