基于自适应单向变分的高光谱图像去条带方法

Hyperspectral Image Destriping Based on Adaptive Unidirectional Variation

条带噪声影响高光谱图像(HSIs)的质量,降低后续数据分析算法的精度和稳健性。分析了HSIs中条带噪声的特点,即条带噪声具有方向性和各谱段噪声具有不同强度,提出了一种基于自适应单向变分的条带噪声去除方法。在单向变分模型的基础上,引入含有耦合项的能量函数,并利用梯度下降法迭代求得最优解。实验结果表明,实际HSIs平均等效视数从26.49提高到85.61,平均辐射质量提升因子提高到9.34dB。与传统方法相比,该方法能够根据不同谱段噪声强度自适应调整正则参数的大小,有效地去除各谱段中的条带噪声,避免细节信息丢失,图像质量得到了改善。

Stripe noise disturbs the quality of hyperspectral images （HSIs）, and decreases the precision and robustness of the downstream data analysis. After analyzing the characteristics of stripe noise of HSIs, that is, stripe noise is directional and noise intensities vary in each band, a new destriping method based on the adaptive unidirectional variation is proposed. On the basis of the unidirectional variation model, an energy function with a coupling term is constructed, which is then optimized iteratively with the gradient descent method. Experimental results demonstrate that the mean equivalent number of looks of real HSIs improves from 26.49 to 85.61, and the mean improvement factor of radiometric quality increases to 9.34 dB. Compared with the conventional methods, the proposed method can adapt to the spectrally varying stripe noise intensities, and is capable of removing stripe noise without loss of detail information and improving the image quality.