Cloud masking of MODIS imagery based on multitemporal image analysis

We present multitemporal Bayesian classification of Moderate Resolution Imaging Spectroradiometer (MODIS) mosaic data over the Caspian Sea during winter. The multitemporal analysis methods used were cross-correlation and motion detection based on phase correlation. Our motion estimation algorithm estimates the motion of a target between two adjacent images over the same areas based on finding the maximum correlation with respect to location shift within a given area around each location. The motion detection algorithm also provides a quality estimate for the detection. Because sea ice, unlike clouds, is typically rigid and its motion is significantly slower than the motion and metamorphosis of clouds, drifting sea ice can be distinguished from clouds. Over land and static sea ice, detection of clouds, is easier because the cross-correlation is typically higher for land and ice than for clouds. Also, locating clouds over open water is straightforward because clouds appear significantly brighter than open water. The results show that multitemporal features can be used to distinguish between clouds and clear sky.