Beta Mixture Model and Boundary Amplification Guided Label Noise Mitigation for Polsar Image Classification

In the field of polarimetric synthetic aperture radar (PolSAR) automatic target classification (ATR), convolutional neural network (CNN) based methods have excelled owing to their adept feature extraction capabilities. However, these methods heavily rely on a sufficiently labeled training dataset for superior classification performance. Limited PolSAR training samples and inevitable noisy labels often render CNNs susceptible to overfitting. To tackle this challenge, a novel PolSAR image classification method employing beta mixture model and boundary amplification is proposed. Initially, the beta mixture model is utilized to fit the loss value distributions of noisy and clean samples, enabling the exploitation of distinct characteristics between these samples for probability estimation. Subsequently, to emphasize boundary samples, the boundary is delineated and expanded using the Sobel operator, amplifying losses for samples within this expanded region. Finally, a robust classification loss function is integrated into the training process to rectify losses incurred by network predictions. Experimental validation conducted on the Flevoland dataset demonstrates that the proposed method attains state-of-the-art performance.