基于薄云厚度分布评估的遥感影像高保真薄云去除方法

High fidelity haze removal method for remote sensing images based on estimation of haze thickness map

针对遥感影像薄云去除易出现地物失真的问题,在传统加性云污染模型的基础上提出了一种改进的薄云去除方法,即基于薄云厚度分布(HTM)评估的高保真薄云去除方法。首先,基于传统的加性薄云去除算法得到HTM,用整个HTM减去HTM中无云区域的平均值,使得HTM满足无云区域的薄云厚度接近于零;然后,对降质影像中的蓝色地物独立估计薄云厚度;最后,用降质影像减去最终优化的不同波段的薄云厚度得到无云影像。对多幅分辨率不同的光学遥感影像进行实验,实验结果表明,所提算法有效解决了蓝色地物失真严重的问题,改进了降质影像的薄云去除效果,提升了在无云区域的数据保真能力。

The haze removal of remote sensing image may easily result in ground object distortion.In order to solve the problem,an improved haze removal algorithm was proposed on the basis of the traditional additive haze pollution model,which was called high fidelity haze removal method based on estimation for Haze Thickness Map(HTM).Firstly,the HTM was obtained by using the traditional additive haze removal algorithm,and the mean value of the cloudless areas was subtracted from the whole HTM to ensure the haze thickness of the cloudless areas closed to zero.Then,the haze thickness of blue ground objects was estimated alone in degraded images.Finally,the cloudless image was obtained by subtracting the finally optimized haze thickness map of different bands from the degraded image.The experiments were carried out for multiple optical remote sensing images with different resolution.The experimental results show that,the proposed method can effectively solve the serious distortion problem of blue ground objects,improve the haze removal effect of degrade images,and promote the data fidelity ability of cloudless areas.