为了有效地去除高分辨卫星图像中薄云的影响,提出了一种基于Mallat小波变换的薄云去除方法。对图像进行Mallat小波分解得到高频细 节部分和低频近似部分,根据云噪声在分解系数中处于低频部分而地物信息占据相对高频部分的特点,在小波变换多尺度分析的基础上, 算法在最大尺度低频图像上按照云厚度掩膜值对云区进行线性处理；对于高频子带图像根据尺度的不同运用非线性增强算子进行 不同程度的增强,从而提高图像的清晰度,减小残留云的影响,之后将重构后的图像进行中值滤波以减少高频云的影响。 针对高分一号卫星图像进行了实验。实验证明,该方法在去除薄云的同时很好地保留了图像细节及边缘信息,去薄云效果优于传统小波变换法。

In order to remove the influence of thin cloud on satellite image effectively, an algorithm based on Mallat wavelet transform is proposed. It’s possible to decompose the image into high frequency detail and low frequency approximation components. Based on the facts that cloud noise occupies lower frequency part in the distribution characteristics while scenery information make up the relative high part, this kind of algorithm processes cloudy zones by using linear methods according to cloud thickness in the largest scale of low frequency sub-band image. Different scales of high frequency sub-band is enhanced by non-linear enhancing operators in order to improve images’ sharpness and reduce the impact of residual cloud. Later median filtering is added to process the reconstruct image to reduce the influence of high frequency mutation cloud. The algorithm is utilized to process GF-1 images. Experiment shows that this kind of algorithm can remove thin cloud while it can also preserve image details and edges the same time, which indicates it is better than traditional wavelet transform method.