Abstract: In order to solve blurred, color deviation, terrain information loss and other serious problems of the remote sensing image captured by the sensor under the conditions of fog days, a remote sensing image defog algorithm（referred to as RSIDA algorithm） is proposed based on saturation operation and dark channel theory. RSIDA algorithm firstly uses weighted quad tree method towards to minimum channel graph to rapidly search for the global ambient light value, and the atmospheric transmittance is estimated by saturation operation in HSV color space, then a threshold is set to adjust the atmospheric transmittance and the recursive bilateral filter is used to optimize it. Finally, fog degraded remote sensing image is recovered based on the physical model. Through the contrast experiments and quantitative analysis, the results show that RSIDA algorithm can recover definition and color of the fog degraded  remote sensing image, improve the quality of the remote sensing image, and its operation efficiency can meet the real-time requirements.

Key words: remote sensing image defog, weighted quad tree, saturation arithmetic, recursive bilateral filter

摘要： 为了解决传感器雾天条件下捕获的遥感影像出现模糊，色彩偏移，地物信息丢失严重等问题，结合饱和度运算和暗通道理论提出一种遥感图像去雾算法（简称RSIDA算法）。RSIDA算法首先采用加权四叉树算法对最小通道图进行快速搜索获取全局环境光值，然后在HSV颜色空间通过饱和度运算估计大气透射率，接着设置阈值调整场景透射率并采用递归双边滤波器对其进行优化，最后通过物理模型恢复雾气降质遥感图像。通过对比实验和定量分析，结果表明RSIDA算法能够较好地恢复雾气降质遥感图像的清晰度和色彩，提高遥感图像的质量，且运算效率能够满足实时性需求。

关键词: 遥感图像去雾, 加权四叉树, 饱和度运算, 递归双边滤波器