# Polarimetric Convolutional Network for PolSAR Image Classification

arXiv: https://arxiv.org/abs/1807.02975 · HTML: https://mlsanity.com/paper/1807.02975/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2018.

## Built on by (most cited first, 6 of 6)

- [A Complex-valued SAR Foundation Model Based on Physically Inspired Representation Learning](https://mlsanity.com/paper/2504.11999/) (2025)
- [Riemannian Complex Matrix Convolution Network for PolSAR Image Classification](https://mlsanity.com/paper/2312.03378/) (2023)
- [PolSAM: Polarimetric Scattering Mechanism Informed Segment Anything Model](https://mlsanity.com/paper/2412.12737/) (2024)
- [ECP-Mamba: An Efficient Multi-scale Self-supervised Contrastive Learning Method with State Space Model for PolSAR Image Classification](https://mlsanity.com/paper/2506.01040/) (2025)
- [Towards Interpretable PolSAR Image Classification: Polarimetric Scattering Mechanism Informed Concept Bottleneck and Kolmogorov-Arnold Network](https://mlsanity.com/paper/2507.03315/) (2025)
- [Multiview Manifold Evidential Fusion for PolSAR Image Classification](https://mlsanity.com/paper/2510.11171/) (2025)

## Abstract (arXiv)

The approaches for analyzing the polarimetric scattering matrix of polarimetric synthetic aperture radar (PolSAR) data have always been the focus of PolSAR image classification. Generally, the polarization coherent matrix and the covariance matrix obtained by the polarimetric scattering matrix only show a limited number of polarimetric information. In order to solve this problem, we propose a sparse scattering coding way to deal with polarimetric scattering matrix and obtain a close complete feature. This encoding mode can also maintain polarimetric information of scattering matrix completely. At the same time, in view of this encoding way, we design a corresponding classification algorithm based on convolution network to combine this feature. Based on sparse scattering coding and convolution neural network, the polarimetric convolutional network is proposed to classify PolSAR images by making full use of polarimetric information. We perform the experiments on the PolSAR images acquired by AIRSAR and RADARSAT-2 to verify the proposed method. The experimental results demonstrate that the proposed method get better results and has huge potential for PolSAR data classification. Source code for sparse scattering coding is available at https://github.com/liuxuvip/Polarimetric-Scattering-Coding.