PCM-SAR: Physics-Driven Contrastive Mutual Learning for SAR Classification
This work addresses SAR image classification for remote sensing applications, representing a domain-specific improvement.
The paper tackles the problem of SAR image classification by addressing the limitations of existing contrastive learning methods that fail to capture SAR-specific characteristics, proposing PCM-SAR which incorporates physics-driven insights for sample generation and feature extraction, resulting in consistent outperformance of state-of-the-art methods across diverse datasets.
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.