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Optimized spatial and spectral decorrelation method for noise estimation in hyperspectral images

  • role： First author第一作者
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

  • Email：zhanglf@radi.ac.cn
  • Introduction：张立福，1967年生，男，研究员， 研究方向为高光谱遥感机理与应用。E-mail：zhanglf@radi.ac.cn
ZHANG Lifu1,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

LU Xuhui12,  
  • role： Corresponding author通信作者
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

  • Email：cenyi@radi.ac.cn
  • Introduction：岑奕，1979年生，女，副研究员，研究方向为高光谱遥感目标探测。E-mail：cenyi@radi.ac.cn
CEN Yi1*,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

SUN Xuejian1

resumen

Noise estimation of hyperspectral images (HSIs) is not only a crucial part of image quality evaluation but is also an important index of sensor performance. Spatial and spectral decorrelation method is a widely used approach for estimating noise in HSIs. This method is based on the high correlation of HSIs in space and spectrum, and a pixel can be predicted well using its spatial and spectral neighbors. Any prediction error can be considered noise. A series of noise estimation algorithms, such as Spatial and Spectral Decorrelation (SSDC), Residual-scaled Local Standard Deviation (RLSD), and Homogeneous Region Division and Spectral Decorrelation (HRDSDC), have been developed on this basis.The images are divided by rule or by some distances between spectrums in the general noise estimated methods. The local standard deviations or the residuals of multiple linear regression of imaging blocks are calculated as the image noise estimation. However, the sub-blocks of the images acquired by these methods are not completely uniform, and the edges of objects are still retained, thereby resulting in inaccurate outcomes of the image noise estimation. To obtain the uniform imaging blocks in the image effectively, an optimized SSDC method for estimating noise in HSIs has been used. The spectral angle and Euclidean distance are used to obtain the uniform imaging blocks, and the residuals of the heterogeneous blocks are calculated by multiple linear regression as the estimation of image noise. The optimized method is validated with simulated and radiance images acquired in the same aerial experiment and is compared with several useful noise estimation methods (e.g., LMLSD, RLSD, SSDC, and HRDSDC). The LMLSD method, which is based on spatial dimension, is susceptible to image texture features and is only suitable for images with relatively uniform landcover. The RLSD method has better noise estimation results than LMLSD.However, the uncertainty of the results is large and cannot indicate the noise level of images accurately. The three methods, namely, SSDC, HRDSDC and OSSDC, are all based on the spatial and spectral dimensions, have high stability, and can be applied to various images. The results of HRDSDC are significantly better than those of SSDC, and the OSSDC method exhibits better performance than HRDSDC. The OSSDC method uses the spectral angle and the Euclidean distance to determine the heterogeneous blocks, which reduce the influence of the edge of objects and the texture features. The results of image noise estimation are also accurate. In the validation, the optimized method shows distinctly enhanced robustness compared with the common methods. The estimation of the noise is also proved to be accurate. In addition, the effect of texture features on noise estimation is discussed in this paper. Results show that larger noise estimation results yield complex texture features.

palabra clave

hyperspectral image;noise estimation;spatial and spectral;de-correlation;image quality assessment;Sensor performance evaluation

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