联合空-谱信息的高光谱图像噪声估计

Noise Estimation Based on Combined Spatial and Spectral Information for Hyperspectral Image

在纹理丰富的高光谱图像中获得精确的噪声估计,是噪声估计任务中的难点。本文基于高光谱图像的空间规律性和光谱相关性,提出一种基于超像素分割的光谱去相关法。同质区域划分是许多噪声估计方法的关键步骤,精确的同质区域划分能有效提高噪声估计精度。为此,将简单线性迭代聚类算法(Simple linear iterative clustering algorithm,SLIC)与光谱-空间相似性结合,划分高光谱图像为局部结构相似的图像块,以保持同质特征;为了提高光谱间的区分能力,将光谱信息散度和光谱角联合作为光谱距离;结合多元线性回归在同质区域内去除光谱相关性,在获得的残差图上估计噪声水平。对不同地物复杂程度的模拟图像,添加不同程度的噪声,通过与多种方法比较,验证了本文方法的有效性和稳定性。最后,本文方法成功应用于Urban数据的噪声水平估计,准确识别出受噪声严重污染的波段。

Obtaining accurate noise estimation in texture-rich hyperspectral images is difficult in the noise estimation task.A spectral decorrelation method based on the spatial regularity and spectral correlation of hyperspectral images is described in this paper.Homogenous region division is a key step in many noise estimation methods,and a precise homogeneous region division can effectively improve the accuracy of noise estimation.To this end,a simple linear iterative clustering algorithm is combined with spectralspatial similarity to segment hyperspectral images into locally structured similar image blocks to maintain homogeneous features.Spectral information divergence and spectral angle are combined as the spectral distance measurement to improve the ability of discrimination between spectra.Spectral correlations are removed within homogeneous regions by multiple linear regression to obtain the noise levels of the residual images.Various degrees of noise are added to simulated images of varying ground complexity,and the effectiveness and stability of this method are verified by comparison with a variety of methods.Finally,the proposed method is successfully applied to noise level estimation of Urban data,and can accurately identify bands heavily polluted by noise.