Cloud Detection of RGB Color Aerial Photographs by Progressive Refinement Scheme

In this paper, we propose an automatic and effective cloud detection algorithm for color aerial photographs. Based on the properties derived from observations and statistical results on a large number of color aerial photographs with cloud layers, we present a novel progressive refinement scheme for detecting clouds in the color aerial photographs. We first construct a significance map which highlights the difference between cloud regions and noncloud regions. Based on the significance map and the proposed optimal threshold setting, we obtain a coarse cloud detection result which classifies the input aerial photograph into the candidate cloud regions and noncloud regions. In order to accurately detect the cloud regions from the candidate cloud regions, we then construct a robust detail map derived from a multiscale bilateral decomposition to guide us in removing noncloud regions from the candidate cloud regions. Finally, we further perform a guided feathering to achieve our final cloud detection result, which detects semitransparent cloud pixels around the boundaries of cloud regions. The proposed method is evaluated in terms of both visual and quantitative comparisons, and the evaluation results show that our proposed method works well for the cloud detection of color aerial photographs.