Remote sensing images are widely used in object detection and tracking, military security, and other computer vision tasks. However, remote sensing images are often degraded by suspended aerosol in the air, especially under poor weather conditions, such as fog, haze, and mist. The quality of remote sensing images directly affect the normal operations of computer vision systems. As such, haze removal is a crucial and indispensable pre-processing step in remote sensing image processing. Additionally, most of the existing image dehazing methods are not applicable to all scenes, so the corresponding dehazed images may have varying degrees of color distortion. This paper proposes a novel atmospheric light estimation based dehazing algorithm to obtain high visual-quality remote sensing images. First, a differentiable function is used to train the parameters of a linear scene depth model for the scene depth map generation of remote sensing images. Second, the atmospheric light of each hazy remote sensing image is estimated by the corresponding scene depth map. Then, the corresponding transmission map is estimated on the basis of the estimated atmospheric light by a haze-lines model. Finally, according to the estimated atmospheric light and transmission map, an atmospheric scattering model is applied to remove haze from remote sensing images. The colors of the images dehazed by the proposed method are in line with the perception of human eyes in different scenes. A dataset with 100 remote sensing images from hazy scenes was built for testing. The performance of the proposed image dehazing method is confirmed by theoretical analysis and comparative experiments.

中文翻译：

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基于大气光估计的遥感图像去雾

遥感图像广泛用于目标检测和跟踪、军事安全和其他计算机视觉任务。然而，遥感图像往往会因空气中悬浮的气溶胶而退化，尤其是在雾、霾、薄雾等恶劣天气条件下。遥感图像的质量直接影响计算机视觉系统的正常运行。因此，去雾是遥感图像处理中至关重要且不可或缺的预处理步骤。此外，现有的大多数图像去雾方法并非适用于所有场景，因此相应的去雾图像可能存在不同程度的颜色失真。本文提出了一种新的基于大气光估计的去雾算法，以获得高视觉质量的遥感图像。第一的，可微函数用于训练线性场景深度模型的参数，用于遥感图像的场景深度图生成。其次，通过对应的场景深度图估计每幅朦胧遥感图像的大气光。然后，通过雾线模型根据估计的大气光估计相应的透射图。最后，根据估计的大气光和透射图，应用大气散射模型去除遥感图像中的雾霾。所提方法去雾后的图像颜色符合人眼在不同场景下的感知。构建了一个包含 100 张来自朦胧场景的遥感图像的数据集用于测试。