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

arXiv: https://arxiv.org/abs/2507.03268 · HTML: https://mlsanity.com/paper/2507.03268/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 59 references that are in the map.
- First posted on arXiv 4 Jul 2025.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [CvT: Introducing Convolutions to Vision Transformers](https://mlsanity.com/paper/2103.15808/) (2021)
- [Transformer in Transformer](https://mlsanity.com/paper/2103.00112/) (2021)
- [Incorporating Convolution Designs into Visual Transformers](https://mlsanity.com/paper/2103.11816/) (2021)
- [Cross Modal Distillation for Supervision Transfer](https://mlsanity.com/paper/1507.00448/) (2015)
- [Joint Token Pruning and Squeezing Towards More Aggressive Compression of Vision Transformers](https://mlsanity.com/paper/2304.10716/) (2023)
- [Decoupled Multimodal Distilling for Emotion Recognition](https://mlsanity.com/paper/2303.13802/) (2023)
- [Multimodal Fusion Transformer for Remote Sensing Image Classification](https://mlsanity.com/paper/2203.16952/) (2022)
- [Beyond Attentive Tokens: Incorporating Token Importance and Diversity for Efficient Vision Transformers](https://mlsanity.com/paper/2211.11315/) (2022)
- [Multimodal Knowledge Expansion](https://mlsanity.com/paper/2103.14431/) (2021)
- [SViTT: Temporal Learning of Sparse Video-Text Transformers](https://mlsanity.com/paper/2304.08809/) (2023)
- [Single-stream CNN with Learnable Architecture for Multi-source Remote Sensing Data](https://mlsanity.com/paper/2109.06094/) (2021)
- [Multi-frequency PolSAR Image Fusion Classification Based on Semantic Interactive Information and Topological Structure](https://mlsanity.com/paper/2209.01921/) (2022)

## Abstract (arXiv)

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.