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Strip noise removal in GF-5 images considering the difference in land and water

  • role： First author第一作者
  • Affiliation：

    School of Resources and Environmental Science,Wuhan University,Wuhan 430079,China

  • Email：Yzhengpi@whu.edu.cn
  • Introduction： 皮原征，1996年生，女，硕士研究生，研究方向为遥感影像条带噪声处理。E-mail: Yzhengpi@whu.edu.cn
PI Yuanzheng1,  
  • Affiliation：

    School of Resources and Environmental Science,Wuhan University,Wuhan 430079,China

CHU Dong1,  
  • Affiliation：

    School of Resources and Environmental Science,Wuhan University,Wuhan 430079,China

GUAN Xiaobin1,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Resources and Environmental Science,Wuhan University,Wuhan 430079,China

    Collaborative Geospatial IT Innovation Center School of Surveying and Mapping,Wuhan 430079，China

  • Email：shenhf@whu.edu.cn
  • Introduction： 沈焕锋，1980年生，男，教授，研究方向为地学感知数据质量改善与融合应用。E-mail:shenhf@whu.edu.cn
SHEN Huanfeng12*

résumé

GF-5 satellite is an important scientific research satellite in China’s high-resolution projects. It is also the first full-spectrum hyperspectral satellite in the world to simultaneously observe the atmosphere and land. GF-5 satellite can meet the urgent needs of China’s environmental monitoring, resource exploration, disaster prevention and mitigation, and other industries. However, similar to many hyperspectral satellite data, random band noises are found in some of its imaging data, thereby reducing the quality of data to a certain extent. The large data width, high spatial resolution, rich detail texture, and heterogeneity of the terrain also establish high requirements for strip removal. In this study, a method of image strip removal considering land–water differences is proposed to robustly remove strip noise in data, restore the real radiation information of the surface, and improve the application value of the GF-5 hyperspectral data.In the proposed algorithm, we use a computationally efficient moment matching algorithm as the basic framework, with the idea of separate treatment between water and land, and combined it with an optimized statistical strategy to obtain many reliable statistical results, and then use 1D variational filtering to obtain a reliable statistical reference value. The moment matching algorithm is used to correct the water and land areas for effectively removing the complex strip noise in the image.Experiments on the L1 data of the GF-5 hyperspectral data without strip preprocessing show that the proposed method can be robustly removed compared with the traditional moment matching, histogram matching, and wavelet Fourier joint filtering methods. Stripe noise in the data, especially in complex scenes, has improved removal. Therefore, the proposed method can effectively solve the high-resolution GF-5 hyperspectral data radiation degradation caused by strip noise, improve the data quality, and utilize its advantages in resource, environment, and ecological applications.This study proposes a global moment matching method based on 1D variational filtering guidance to address the band noise in GF-5 hyperspectral image data. This algorithm is based on the moment matching algorithm and uses different statistics between water and land to overcome the unreliable statistical characteristics of the bands in heterogeneous regions. 1D variational filtering technology is used to obtain water and land regions. Domains have accurate statistical reference values. The experimental results show that the proposed method can robustly remove band noise in data, effectively solve the problem of radiation degradation caused by high-score band noise, and improve the data quality. In the next step, the abundant spectral dimension information of GF-5 hyperspectral image data is utilized to correct the image data polluted by noise and improve the strip noise removal result.

mots-clés

stripe noise;one-dimensional filtering;variation;moment matching method;GF-5 hyperspectral image;image restoration;remote sensing of hyperspectral imagery

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