Radon single-pixel flying target classification via texture-fused lightweight differentiable operators.

Due to its rapid sampling speed and long-range imaging capabilities, Radon single-pixel imaging (SPI) has unique advantages in birds monitoring and counter-drone applications, such as airport security protection. Additionally, the Radon SPI algorithm can further enhance imaging speed by reducing the sampling rate, enabling better tracking and capturing of fast-moving aerial objects. However, low sampling rates also significantly degrade imaging quality, posing challenges for content recognition. For computational simplicity, most current SPI classification algorithms rely on shallow and small networks with limited discriminative power. However, with the rapid development of GPU hardware and deep learning techniques, using advanced deep models for ultra-low-sampling-rate Radon SPI classification has become a promising solution. This paper builds upon state-of-the-art (SOTA) lightweight classification models and fully leverage prior knowledge of Radon SPI characteristics. By integrating traditional texture operators and line-filtering operators into differentiable modules, an efficient classification model is specifically designed and optimized for Radon SPI. Experiments on a built Radon SPI flying target classification dataset demonstrate that the proposed model achieves the highest Top-1 accuracy compared to SOTA lightweight classification models. The source code will be made public.