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首页> 外文期刊>ISPRS Journal of Photogrammetry and Remote Sensing >An effective thin cloud removal procedure for visible remote sensing images
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An effective thin cloud removal procedure for visible remote sensing images

机译:有效的薄云去除程序,用于可见遥感图像

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摘要

Clouds are obstructions for land-surface observation, which result in the regional information being blurred or even lost. Thin clouds are transparent, and images of regions covered by thin clouds contain information about both the atmosphere and the ground. Therefore, thin cloud removal is a challenging task as the ground information is easily affected when the thin cloud removal is performed. An efficient and effective thin cloud removal method is proposed for visible remote sensing images in this paper, with the aim being to remove the thin clouds and also restore the ground information. Since thin cloud is considered as low-frequency information, the proposed method is based on the classic homomorphic filter and is executed in the frequency domain. The optimal cut-off frequency for each channel is determined semi-automatically. In order to preserve the clear pixels and ensure the high fidelity of the result, cloudy pixels are detected and handled separately. As a particular kind of low-frequency information, cloud-free water surfaces are specially treated and corrected. Since only cloudy pixels are involved in the calculation, the method is highly efficient and is suited for large remote sensing scenes. Scenes including different land-cover types were selected to validate the proposed method, and a comparison analysis with other methods was also performed. The experimental results confirm that the proposed method is effective in correcting thin cloud contaminated images while preserving the true spectral information.
机译:云是陆地表面观测的障碍物,导致区域信息变得模糊甚至丢失。薄云是透明的,并且被薄云覆盖的区域的图像包含有关大气和地面的信息。因此,去除薄云是一项艰巨的任务,因为执行去除薄云时容易受到地面信息的影响。提出了一种有效,有效的薄云去除方法,用于可见遥感图像的去除,以去除薄云并恢复地面信息。由于薄云被视为低频信息,因此所提出的方法基于经典的同态滤波器,并在频域中执行。每个通道的最佳截止频率是半自动确定的。为了保留清晰的像素并确保结果的高保真度,请分别检测并处理浑浊的像素。作为一种特殊的低频信息,无云水面经过特殊处理和校正。由于仅多云像素参与计算,因此该方法高效且适用于大型遥感场景。选择包括不同土地覆盖类型的场景来验证该方法,并与其他方法进行了比较分析。实验结果证实了该方法在校正薄云污染图像的同时,还能保留真实的光谱信息。

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