предыдущий|следующий

Noise estimation of hyperspectral image in the spatial and spectral dimensions

  • role： First author第一作者
  • Affiliation：

    College of Electrical and Information Engineering, Hunan University, Changsha 410082, China

  • Email：z_shuo@hnu.edu.cn
  • Introduction：章硕,1996年生,女,博士研究生, 研究方向为高光谱图像处理。E-mail: z_shuo@hnu.edu.cn
ZHANG Shuo,  
  • Affiliation：

    College of Electrical and Information Engineering, Hunan University, Changsha 410082, China

SUN Bin,  
  • Affiliation：

    College of Electrical and Information Engineering, Hunan University, Changsha 410082, China

LI Shutao,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Electrical and Information Engineering, Hunan University, Changsha 410082, China

  • Email：xudong_kang@163.com
  • Introduction：康旭东,1985年生,男,教授, 研究方向为遥感图像处理与应用。E-mail: xudong_kang@163.com
KANG Xudong*

реферат

Given the influence of imaging environment and equipment limits, hyperspectral images (HSIs) are often disturbed by noise. Thus, denoising is necessary for the subsequent image processing. Noise type and level are important parameters of the denoising algorithm. Furthermore, noise estimation can help people understand the image quality objectively. Many HSI noise estimation algorithms consider images to contain additive noise and measure the level of noise by estimating the statistical characteristics, such as standard deviation and co-variance. To our knowledge, the existing HSI noise estimation methods do not consider the specific type of noise. Unlike previous works, we propose a method that combines spatial and spectral domain analyses for the separation and estimation of different types of noise.Considering the characteristics of HSI noise, the noise contained in a HSI is modeled in this work as the linear combination of stripe and Gaussian noise. According to the spatial characteristics (horizontal and vertical distribution) of stripe noise, after Fourier transform, stripe noise can be represented as a specific central cross line distribution in the Fourier spectrum map. Therefore, stripe noise can be separated and quantitatively estimated by processing the pixels on the cross line. However, the useful information in the HSI may also distribute on the central cross line in the Fourier spectrum map. To eliminate the estimation error of stripe noise, the heterogeneity function is introduced to decrease the estimation error, and local mean filtering is used to further separate stripe noise and useful signal. The level of stripe noise is estimated by the sum of the pixel values on the cross line. After removing the stripe noise, a method based on multiple regression is used to extract the Gaussian noise in an image. Using the correlation among adjacent bands and the randomness of noise, a single band image can be represented by the linear combination of the remaining bands and the residual. Given that residuals can be approximately represented as a Gaussian distribution, the mean and standard deviation of the extracted residuals are calculated to describe the distribution characteristics of Gaussian noise in different bands.In the simulation experiment, image bands with stripe noise were detected successfully; the estimation Gaussian noise standard deviation is 0.0527 (theoretical value is 0.05), the mean value is near the theoretical value of 0. Furthermore, seven HSIs captured by satellites GF-5 and airborne hyperspectral imager Nano-Hyperspec were tested. The estimation results of real-world HSIs show that the mean of Gaussian noise is very near 0 for each band, which is consistent with the assumption in most denoising algorithms. For the stripe noise, some distribution rules of stripe noise are provided.In this study, we proposed a noise estimation method based on Fourier transform and multiple linear regression. This method can separate and estimate the level of the two types of noise. Experimental results show that the proposed method is efficient, and the noise levels of a HSI vary in different bands, sensors, and scenes. More importantly, the noise properties of HSIs were analyzed in this work. Some conclusions about the characteristic of HSI noise can be obtained.

ключеви́че слова́

hyperspectral image;noise estimation;noise separation;fourier transform;stripe noise;Gaussian noise

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