Cloud detection from thermal infrared images using a segmentation technique

In this paper, a fast and accurate method is proposed for two cloud detection tests on thermal infrared (IR) data obtained from satellite images over sea; an IR gross cloud test and a spatial coherence test. The proposed method is based on a regional segmentation technique. After the segmentation of an IR image, small regions were regarded as cloudy due to their high spatial variability in temperature. This technique preserved the spatial resolution of the detected cloud image which would be degraded by the conventional spatial coherence test. It also reduced the computation dramatically, compared to the conventional spatial coherence test. An accurate temperature threshold between clear sea and clouds was determined directly from the segmented image. This post-determined threshold was found to be more accurate than pre-determined temperature thresholds. Since this algorithm does not require any human interaction, it can be combined with other tests in an automatic cloud detection algorithm.