# PCM-SAR: Physics-Driven Contrastive Mutual Learning for SAR Classification

arXiv: https://arxiv.org/abs/2504.09502 · HTML: https://mlsanity.com/paper/2504.09502/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 29 references that are in the map.
- First posted on arXiv 13 Apr 2025.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution](https://mlsanity.com/paper/2202.10054/) (2022)
- [Adaptive Graph Contrastive Learning for Recommendation](https://mlsanity.com/paper/2305.10837/) (2023)
- [Dual-stream contrastive predictive network with joint handcrafted feature view for SAR ship classification](https://mlsanity.com/paper/2311.15202/) (2023)
- [Double Reverse Regularization Network Based on Self-Knowledge Distillation for SAR Object Classification](https://mlsanity.com/paper/2311.15231/) (2023)
- [Semantic-Aware Autoregressive Image Modeling for Visual Representation Learning](https://mlsanity.com/paper/2312.10457/) (2023)

## Abstract (arXiv)

Existing SAR image classification methods based on Contrastive Learning often rely on sample generation strategies designed for optical images, failing to capture the distinct semantic and physical characteristics of SAR data. To address this, we propose Physics-Driven Contrastive Mutual Learning for SAR Classification (PCM-SAR), which incorporates domain-specific physical insights to improve sample generation and feature extraction. PCM-SAR utilizes the gray-level co-occurrence matrix (GLCM) to simulate realistic noise patterns and applies semantic detection for unsupervised local sampling, ensuring generated samples accurately reflect SAR imaging properties. Additionally, a multi-level feature fusion mechanism based on mutual learning enables collaborative refinement of feature representations. Notably, PCM-SAR significantly enhances smaller models by refining SAR feature representations, compensating for their limited capacity. Experimental results show that PCM-SAR consistently outperforms SOTA methods across diverse datasets and SAR classification tasks.