Polarimetric SAR image terrain classification based on superpixel and LightGBM

WANG Yize; SUN Jili; YAN Chengjie; ZHANG Zheng

doi:10.7523/j.ucas.2022.023

Journal of University of Chinese Academy of Sciences >

2023 , Vol. 40 >Issue 5: 658 - 669

DOI: https://doi.org/10.7523/j.ucas.2022.023

Research Articles

Polarimetric SAR image terrain classification based on superpixel and LightGBM

WANG Yize ,
SUN Jili ,
YAN Chengjie ,
ZHANG Zheng

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1. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China;
2. School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China

Received date: 2022-01-12

Revised date: 2022-03-25

Online published: 2022-03-25

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Abstract

Speckle noise of polarimetric synthetic aperture radar(SAR) image reduces the accuracy of terrain classification. Combine multiple features of polarimetric SAR image to do classification, and the large dimension of input features consumes too much time. To handle the above problems, we propose a classification algorithm based on superpixel and LightGBM. With polarimetric features and texture features, the algorithm is good at classification. LightGBM is used to deal with large dimension of input features, which can obtain the pixel-based first-level classification result efficiently. SLIC is used to generate the superpixel-based polarimetric SAR image, and the superpixel-based two-level classification result is obtained by voting pixel by pixel in each superpixel, which solves the influence of speckle noise. Experimental results, using the measured polarimetric SAR data, show that the overall classification accuracy is more than 97%, and it has a low time-consuming.

Key words： polarimetric SAR; terrain classification; superpixel; SLIC; LightGBM

Cite this article

WANG Yize , SUN Jili , YAN Chengjie , ZHANG Zheng . Polarimetric SAR image terrain classification based on superpixel and LightGBM[J]. Journal of University of Chinese Academy of Sciences, 2023 , 40(5) : 658 -669 . DOI: 10.7523/j.ucas.2022.023

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