Color-based Segmentation of Sky/Cloud Images From Ground-based Cameras
Automatic contrail detection is of major importance in the study of the atmospheric effects of aviation. Due to the large volume of satellite imagery, selecting contrail images for study by hand is impractical and highly subject to human error. It is far better to have a system in place that will automatically evaluate an image to determine 1) whether it contains contrails and 2) where the contrails are located. Preliminary studies indicate that it is possible to automatically detect and locate contrails in Advanced Very High Resolution Radiometer (AVHRR) imagery with a high degree of confidence. Once contrails have been identified and localized in a satellite image, it is useful to segment the image into contrail versus noncontrail pixels. The ability to partition image pixels makes it possible to determine the optical properties of contrails, including optical thickness and particle size. In this paper, we describe a new technique for segmenting satellite images containing contrails. This method has good potential for creating a contrail climatology in an automated fashion. The majority of contrails are detected, rejecting clutter in the image, even cirrus streaks. Long, thin contrails are most easily detected. However, some contrails may be missed because they are curved, diffused over a large area, or present in short segments. Contrails average 2–3 km in width for the cases studied.