Detection of cumulus cloud fields in satellite imagery

Boundary layer cumulus clouds are hard to detect in satellite imagery, especially for GOES imagery due to the coarse resolution of the infrared channels. Two different approaches for the detection of cumulus clouds in GOES satellite imagery are discussed and intercompared. The first type, structural thresholding, uses the morphology of cumulus cloud fields for detection. The second type, uses 1)classifiers based on texture and spectral, 2)edge detection and spectral, and 3)purely spectral features. For five selected scenes, cumulus cloud masks are created using these various methods and are compared against the expert-labeled masks. The structural thresholding method has the highest percentage of correct classification (764b), followed by classifier based on Laplacian edge detection features (74%). The classification time is lowest for the structural thesholding method, followed by classifiers based on spectral, edge detection, textural features. The structural thresholding method also is capable of detecting individual cumulus clouds within cloud fields. For the five scenes investigated, the average percentage of correct labeling of cumulus clouds by the structural thresholding method is 86%.