Polarimetric Convolutional Network for PolSAR Image Classification

TL;DR

This paper introduces a novel polarimetric convolutional network that employs sparse scattering coding to fully utilize polarimetric information for improved PolSAR image classification.

Contribution

It proposes a new sparse scattering coding method combined with a convolutional neural network for enhanced PolSAR image classification.

Findings

Outperforms existing methods on AIRSAR and RADARSAT-2 datasets.

Effectively preserves polarimetric information during encoding.

Demonstrates significant accuracy improvements in classification results.

Abstract

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.