Spectral classification of ecological spatial polarization SAR image based on target decomposition algorithm and machine learning

  • Guobin Chen
  • Lukun WangEmail author
  • M. M. Kamruzzaman
ATCI 2019


With the development of science and technology, the classification of polarimetric SAR images has become an important part of the research of target recognition and image interpretation. However, for the research method is relatively simple and the accuracy is low, this paper carries out the work from the two aspects of feature extraction and feature classification of the ground object, and analyzes and studies the application and value of the polarimetric SAR system. The basic algorithm of polarization SAR image classification is proposed. A polarimetric SAR image feature classification method based on polarization target decomposition and support vector machine is proposed. Four kinds of scattering features and Freeman decomposition are obtained by Cloude decomposition. The simulation results show that the accuracy of using combined features is about 6.5% higher than that of single features. A polarization classification model based on polarization target decomposition and limit learning method is proposed. The simulation experiment shows ELM learning. The algorithm is indeed much faster than SVM learning. In this paper, a polarimetric SAR image classification method based on improved scattering mechanism coefficients is proposed, and the effectiveness of the polarimetric SAR image classification method based on improved scattering mechanism coefficients is verified. Experimental results show that after feature selection, the method of combining Freeman decomposition and Wishart classifier can get better classification results.


Polarimetric SAR Target decomposition Machine learning Image feature classification Extreme learning 



This work was supported by the Science and Technology Research Program of Chongqing Municipal Education Commission (Nos. KJQN201802101, KJZD-K201802101); the Doctoral high school talent training project (BYJS2016003); the Chongqing Graduate Scientific Research Innovation Project (CYB17131); the National Natural Science Foundation of China under Grants (71473074); and the National Natural Science Foundation of Shandong Province (ZR2018BF005). Jouf University, Sakaka, Al-Jouf, KSA (40/140).

Compliance with ethical standards

Conflict of interest

The authors declared that they have no conflicts of interest to this work.


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Chongqing Key Laboratory of Spatial Data Mining and Big Data Integration for Ecology and EnvironmentRongzhi College of Chongqing Technology and Business UniversityChongqingChina
  2. 2.Department of Information EngineeringShandong University of Science and TechnologyTai’anChina
  3. 3.Department of Computer and Information ScienceJouf UniversitySakakaKingdom of Saudi Arabia

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