Ground-Based Cloud Detection Using Graph Model Built Upon Superpixels

Abstract

Cloud detection plays an important role in climate models, climate predictions, and meteorological services. Although researchers have given increasing efforts on cloud detection, the performance is still unsatisfactory due to the diverse nature of clouds. Considering the fact that one source of information (color or texture) is not enough to segment cloud from clear sky, in this letter, we propose a novel ground-based cloud detection method using graph model (GM) built upon superpixels to integrate multiple sources of information. First, we use the superpixel segmentation to divide the image into a series of subregions according to the color similarity and spatial continuity. Next, adjacent superpixels are merged according to their similarity of extracted features. Finally, we build a GM on the merged superpixels by considering each superpixel as a node and adding edges between neighboring ones. The unary cost is set according to the classification score of Random Forests, while pairwise cost reflects the penalties for color and texture discontinuity between neighboring components. The final segmentation could be acquired by minimizing the cost function. Moreover, the algorithm is computationally efficient as we use the superpixels rather than raw pixels as computation units. Experimental results demonstrate the effectiveness and efficiency of the proposed method for cloud detection.