Quantum-Inspired Robust and Scalable SAR Object Classification
This work addresses the challenges of robustness and model lightweighting in synthetic aperture radar (SAR) image classification, which arise from strong noise, high dynamic range, and edge deployment constraints. For the first time, the study introduces tensor networks inspired by quantum computing to construct a lightweight classification model. By integrating robust training strategies, the approach effectively enhances model stability under data poisoning attacks and in high-noise environments. Experimental results demonstrate that the proposed method significantly reduces model size while maintaining high classification accuracy and exhibits superior resilience to interference compared to conventional neural networks.
0 citationsRead paper