基于同质区域分割的高光谱图像混合噪声估计

Hybrid Noise Estimation Based on Homogeneous Regions Segmentation in Hyperspectral Images

降低高光谱(Hyperspectral,HS)图像中的噪声以提高图像质量一直是遥感图像处理领域的研究热点,而HS图像带有的混合光电噪声却难于准确估计,为此提出一种基于同性质区域(Homogeneous Region,HR)分割的HS图像混合噪声估计方法。首先结合HS图像的空间和光谱特性进行HR分割,然后在HR内通过多元线性回归(Multiple Linear Regression,MLR)方法去除区域相关性从而得到混合噪声,最后引进比例因子对混合噪声的内部参数进行估计。通过在仿真HS数据和真实AVIRIS数据上进行实验表明,该方法能够有效地进行HR分割,且对混合噪声的估计结果要优于其它传统噪声估计方法。

Reducing the noise in hyperspectral （HS） images to enhance image quality has been a hot research field of remote sensing image processing, but hybrid optoeleetronie noise is difficult to precisely estimate. A new method for the HS hybrid noise estimation based on the homogeneous regions （HR） segmentation is proposed. The method makes HR segmentation by combining with spatial and spectral characteristics of the HS image first, and then removes correlation in regions by using multiple linear re- gression （MLR） to get the hybrid noise. Finally, the scale factor is introduced to estimate the internal parameters of the hybrid noise. The performance of this method is analyzed on simulated HS data and also applied to a well-known airborne visible infrared imaging spectrometer （AVIRIS） data. The experiment demonstrates this method improves the accuracies of segmentation and hy- brid noise estimation when compared to other approaches.