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Semi-supervised Classification of PolSAR Image Based on Self-training Convolutional Neural Network

  • Xianxiang QinEmail author
  • Wangsheng Yu
  • Peng Wang
  • Tianping Chen
  • Huanxin Zou
Conference paper
  • 18 Downloads
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 657)

Abstract

Convolutional neural networks (CNNs) have been successfully used in the field of polarimetric synthetic aperture radar (PolSAR) image classification. However, it is still a big challenge to perform them with limited labeled training samples. To address this problem, a semi-supervised scheme of PolSAR image classification based on self-training CNN is proposed in this letter. Our basic idea is that the PolSAR image to be classified is actually an important source of training samples, from which we iteratively extract reliable samples to enhance the CNN. In our scheme, a CNN is initially trained by the given training samples. Then, the PolSAR image is classified by the trained CNN, of which the pixels with high predicted probability, along with their predicted labels, are selected as new training samples. The operations of retraining CNN, reclassifying PolSAR image and reselecting training samples, are iteratively performed until a stop condition is met. Two actual PolSAR images acquired by AIRSAR and Gaofen-3 systems are employed to verify the effectiveness of the proposed algorithm. Experiment results demonstrate the superiority of our method to the general CNN-based classifiers.

Keywords

Polarimetric synthetic aperture radar Semi-supervised classification Self-training convolutional neural network 

Notes

Acknowledgements

This work is supported by National Science Foundation of China under Grant 41601436, 61403414 and 61773396 and in part by the Natural Science Basic Research Plan in Shaanxi Province of China under Grant 2018JM4029 and 2019JM-554.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Xianxiang Qin
    • 1
    Email author
  • Wangsheng Yu
    • 1
  • Peng Wang
    • 1
  • Tianping Chen
    • 1
  • Huanxin Zou
    • 2
  1. 1.Information and Navigation CollegeAir Force Engineering UniversityXi’anChina
  2. 2.College of Electronic ScienceNational University of Defense TechnologyChangshaChina

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