Ground-Based Cloud Detection Using Automatic Graph Cut

Ground-based cloud detection plays an essential role in meteorological research, and object segmentation techniques have recently been introduced to solve this issue. As a kind of object segmentation technique, interactive graph cut has emerged as a very powerful tool due to its effective segmentation ability. However, it requires users to provide labels for certain pixels as "object" or "background," which inevitably prohibits automatic cloud detection in large-scale applications. In this letter, we focus on the issue of automatic cloud detection and propose a novel algorithm named as automatic graph cut. We treat clouds as a special kind of object and eliminate human labeling by two procedures. First, we adaptively compute the thresholds for each cloud image which automatically label some pixels as "cloud" or "clear sky" with high confidence. Then, those labeled pixels serve as hard constraint seeds for the following graph cut algorithm. The experimental results show that the proposed algorithm not only achieves better results than the state-of-the-art cloud detection algorithms but also achieves comparable results with the interactive segmentation algorithm.