噪声调节主成分变换下的遥感图像薄云去除撤回

Thin cloud removal in remote sensing images based on noise-adjusted principal component transform

针对在光学遥感图像中的地球表面反射率由于云覆盖所造成的衰减问题,本文提出了一种基于噪声调整主成分变换(NAPCT)模型的薄云去除方法。首先,通过噪声估计构建NAPCT除云模型,实现云图像的转换;然后,利用NAPCT第一成分(NAPC1)提供的云分布信息,通过合并云掩膜,对NAPC1上的浑浊像素进行校正;最后,将逆变换应用于云覆盖区域,并与原始的清晰像素镶嵌在一起,最终得到无云图像。利用模拟和真实的Landsat 8图像对本文方法的性能进行了定性和定量评估,试验结果表明:与传统方法相比,本文NAPCT方法提供了更好的均方根误差和峰值信号噪声比,在除云上具有更好的效果。

To solve the problem of attenuation of the earth’s surface reflectance in optical remote sensing images due to cloud cover,a thin cloud removal method based on the noise-adjusted principal component transform( NAPCT) model is proposed. Firstly,a NAPCT cloud removal model is constructed through noise estimation to realize the conversion of cloud images. Secondly,it corrects the turbid pixels on NAPC1 by using the cloud distribution information provided by the first component of NAPCT( NAPC1) and merging the cloud mask. Finally,the inverse transform is applied to the cloud covered area and mosaic with the original clear pixels to obtain a cloudless image. The performance of the method proposed in this paper is evaluated qualitatively and quantitatively using simulated and real Landsat 8 images. The experimental results show that the NAPCT method proposed in this paper provides better uniformity compared with other methods. The root square error and the peak signal-to-noise ratio have a better effect on removing clouds.