Journal of Geodesy and Geoinformation Science ›› 2022, Vol. 5 ›› Issue (4): 72-85.doi: 10.11947/j.JGGS.2022.0407

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An Effective Strip Noise Removal Method for Remote Sensing Image

Chang WANG1,2(),Yongsheng ZHANG2(),Xu WANG3,Song JI2   

  1. 1. School of Civil Engineering, University of Science and Technology Liaoning, Anshan 114051, China
    2. Institute of Surveying and Mapping, Information Engineering University, Zhengzhou 450001, China
    3. Forestry Institute, Liaoning Vocational College of Ecological Engineering, Shenyang 110101, China
  • Received:2022-06-15 Accepted:2022-09-15 Online:2022-12-20 Published:2023-03-15
  • Contact: Yongsheng ZHANG
  • About author:Chang WANG (1983—), male, PhD student, lecturer, majors in remote sensing image processing. E-mail:
  • Supported by:
    National Natural Science Foundation of China(41671409);National Natural Science Foundation of China(41401534)


In this paper, an efficient technique for removing strip noise from remote sensing images is proposed in order to better retain image details. Firstly, the remote sensing image with strip noise is decomposed by wavelet technology; Secondly, two variational models are constructed, stripe preserve variation model and a destriping variation model. In order to efficiently separate the detail information in the low level high-frequency component, the stripe preserve variation model eliminates the detail information from the low level high-frequency component (including strip noise) while maintaining the strip noise (including strip noise). In order to successfully save the details in the high level high-frequency component, the destriping variation model eliminates the strip noise in the high level high-frequency component (including the strip noise). Finally, wavelet reconstruction is used to get the denoised image. It is clear from a comparison with previous approaches that the suggested method not only successfully removes strip noise but also preserves image details.

Key words: wavelet transform; stripe preserve variation model; destriping variation model; high-frequency component; remote image