In this work, we address the problem of Cumulonimbus (Cb) cloud detection from Infrared (IR) satellite images. The Cb Clouds are associated with thunderstorms and atmospheric instability and their detection is of high importance since they pose extreme danger to aviation. We present a joint spatio-temporal detection method that exploits the distinct spatial characteristics of Cb clouds as well as their prototypical evolution over time. The presented method is unsupervised and does not require labeled data or predefined spatial handcrafted features, such as particular shapes, temperatures, textures, and gradients. We demonstrate the performance of the proposed method on several sequences of IR satellite images taken from the middle east region. Our method outperforms other approaches we compared it to.
Spatio-Temporal Detection of Cumulonimbus Clouds in Infrared Satellite Images, 2018 International Conference on the Science of Electrical Engineering (ICSEE), Eilat, Israel.,
Best Student Paper Award