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

Source: [PolSAR Image Classification based on Polarimetric Scattering Coding and Sparse Support Matrix Machine](<https://www.sota2.com/research/paper/68fa65df1e209131cea0d05e>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:37:37.790Z

Authors: Xu Liu, Licheng Jiao, Dan Zhang, Fang Liu

Published: 2019-06-17T03:17:13.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1906.07176>)

[PDF](<https://arxiv.org/pdf/1906.07176.pdf>)
