基于光谱-空间信息的高光谱遥感图像混合噪声评估

A spectral-spatial information based approach for the mixed noise estimation from hyperspectral remote sensing images

综合利用高光谱图像的光谱信息和空间信息,提出了一种新的混合噪声评估方法.首先通过滤波算法进行图像中均匀图像块的自动选取;然后利用多元线性回归模型,将均匀图像块内像素点的信号值和噪声值进行分离,并实现了图像中加性、乘性噪声的粗评估;最后根据噪声模型构建似然函数,利用最大似然估计法求解噪声模型参数.通过仿真图像和真实高光谱图像进行实验,验证了该方法的准确性和鲁棒性.

A novel mixed noise estimation method based on the spectral and spatial information of hyperspectral images was reported. Firstly, homogeneous image blocks were automatically detected using data masking. Then signal value and noise value of each pixel in homogeneous blocks were split with a multiple liner regression model. Meanwhile, rough ap- proximations of SD and SI noise were obtained. Finally, likelihood function was built based on the mixed noise model, where parameters of the noise model were calculated by maximum-likelihood estimation approach. The proposed method is demonstrated to be accurate and robust by experiments with both synthetic images and real hvoersoectral images.