收稿：2020-08-09，
纸质出版：2021-05-07
移动端阅览
高光谱图像能够获取地物精细的光谱诊断特征，但受限于多谱段分光的成像机制，图像各个谱段上光成像的能量不足，信噪比难以提升。高光谱图像噪声类型与强度的准确估计，是提升高光谱图像去噪性能的关键，也是优化其成像系统设计的重要依据。现有高光谱图像噪声估计算法通常将不同类型的图像噪声作为一个整体，并未充分考虑不同类型噪声的区别。本文从高光谱图像获取的机理出发，提出了一种联合空间与光谱维度分析的高光谱图像噪声估计方法。首先，建立了高光谱图像噪声退化模型，将图像中的主要噪声定义为两类：条带与高斯噪声。然后，基于条带噪声在空间维度上独特的频率特性，提出了基于傅里叶变换与局部均值滤波的条带噪声估计方法。最后，基于在光谱维度上高光谱图像相邻波段间的高相关性，通过多元回归分析估计高斯噪声的均值与标准差。本文在模拟高光谱噪声数据上进行算法验证的同时，深入分析了高分五号短波红外高光谱相机、机载Nano-Hyperspec成像仪等国内外成像仪获取的真实高光谱数据。实验结果表明，本文提出的噪声估计方法能够有效的估计出高光谱图像不同谱段条带与高斯噪声的量化指标。实验结果可用于分析高光谱图像在不同传感器与不同成像场景下的退化原因，从而设计更优的图像去噪方法与成像系统。
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.
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