# PolSAR Image Classification based on Polarimetric Scattering Coding and Sparse Support Matrix Machine

## TL;DR

This paper introduces a new POLSAR image classification method that leverages polarimetric scattering coding and a sparse support matrix machine, improving classification accuracy by effectively utilizing the data's sparse and polarimetric properties.

## Contribution

The paper proposes a novel classification approach combining polarimetric scattering coding with a sparse support matrix machine, enhancing POLSAR image interpretation.

## Key findings

- Achieves better classification accuracy than existing methods.
- Effectively exploits the sparsity and polarimetric features of POLSAR data.
- Provides a robust classification framework for POLSAR images.

## Abstract

POLSAR image has an advantage over optical image because it can be acquired independently of cloud cover and solar illumination. PolSAR image classification is a hot and valuable topic for the interpretation of POLSAR image. In this paper, a novel POLSAR image classification method is proposed based on polarimetric scattering coding and sparse support matrix machine. First, we transform the original POLSAR data to get a real value matrix by the polarimetric scattering coding, which is called polarimetric scattering matrix and is a sparse matrix. Second, the sparse support matrix machine is used to classify the sparse polarimetric scattering matrix and get the classification map. The combination of these two steps takes full account of the characteristics of POLSAR. The experimental results show that the proposed method can get better results and is an effective classification method.