目的 高光谱遥感图像常存在多种不同程度的退化,进而影响到后续的应用,因此,对高光谱图像进行噪声水平估计具有重要意义。在实际情况中,不同波段的图像噪声水平常有所差异,需要针对不同谱通道的特性差异进行噪声估计。因此,本文提出一种基于低秩表达的噪声水平估计算法。方法 该算法首先利用多波段图像间的光谱相关性,建立高光谱数据的低秩表达模型;再通过该模型对各波段的噪声及其水平进行估计,并根据需要检测并剔除被噪声淹没的无效波段。结果 在多组高光谱数据上进行模拟和真实实验,证明本文算法能够准确估计高光谱图像的谱通道噪声水平。结论 本文算法挖掘了低秩表达在高光谱应用中的特性,在利用波段间相关性进行全局处理的同时,也能保留波段间的差异,具有较强的鲁棒性;在合适的阈值范围内,无效波段的漏检率低至0,准确率高于80%。

Objective Most hyperspectral remote sensing images suffer from degradation because of the distortion of atmospheric transmission, the limitation of electron devices, and the influence of poor illumination. As a result, the performance of these images in subsequent applications is seriously affected. Thus, the noise in hyperspectral images must be estimated. Given that the noise levels in different bands are often not equivalent in practice, the noise level in each band must be estimated to select an efficient subset of bands. To achieve this end, this paper proposes a hyperspectral image noise estimation algorithm. Method First, given the high correlation between hyperspectral channels, a low-rank-based model is established specifically for the hyperspectral case. A proper furthermore rule is selected for the noise estimation model to achieve robust performance. Second, the noise in hyperspectral channels is estimated simultaneously using the proposed model. Third, the noise density in each band is calculated as noise level, and the useless bands can be rejected. Result Experiments are performed on both simulated and real datasets. The proposed method is more robust and can achieve better results than several existing methods because it fully utilizes the correlation and difference between bands. Conclusion Given that the noise level in bands may be unbalanced, this paper proposes a noise estimation algorithm for hyperspectral images by exploiting the low-rank characteristic of hyperspectral data. By considering noise analysis, this paper proposes a new method to evaluate the quality of hyperspectral images without reference. The proposed algorithm can be applied to highly correlated multichannel images, and the evaluation results are in accordance with expert knowledge and manual interpretation.