Dual-frequency Selected Knowledge Distillation with Statistical-based Sample Rectification for PolSAR Image Classification

TL;DR

This paper introduces SKDNet-SSR, a novel dual-frequency PolSAR image classification method that employs statistical sample rectification and knowledge distillation to improve accuracy and robustness.

Contribution

The paper proposes a new framework combining statistical-based sample rectification and dual-frequency knowledge distillation for enhanced PolSAR image classification.

Findings

Outperforms existing methods on four dual-frequency PolSAR datasets.

Effectively removes noisy pixels, improving feature extraction.

Enhances dual-frequency data utilization for better terrain classification.

Abstract

The collaborative classification of dual-frequency PolSAR images is a meaningful but also challenging research. The effect of regional consistency on classification information learning and the rational use of dual-frequency data are two main difficulties for dual-frequency collaborative classification. To tackle these problems, a selected knowledge distillation network with statistical-based sample rectification (SKDNet-SSR) is proposed in this article. First, in addition to applying CNN and ViT as local and global feature extractors, a statistical-based dynamic sample rectification (SDSR) module is designed to avoid the impact of poor regional consistency on spatial information learning process. Specifically, based on the fact that the PolSAR covariance matrix conforms to the complex Wishart distribution, SDSR first dynamically evaluates the sample purity, and then performs pixel selection and pixel generation to remove noisy pixels, thereby avoiding the feature interaction between informative pixels and noisy pixels and improving the classification feature extraction process. Next, a dual-frequency gate-selected distillation (DGSD) module is constructed to emphasize the advantages of different frequency bands and perform complementary learning on dual-frequency data. It uses the dominant single-frequency branch on each sample as teacher model to train the dual-frequency student model, enabling the student model to learn the optimal results and realizing complementary utilization of dual-frequency data on different terrain objects. Comprehensive experiments on four measured dual-frequency PolSAR data demonstrate that the proposed SKDNet-SSR outperforms other related methods.