A novel visualization and tracking framework for analyzing the inter/intra cloud pattern formation to study their impact on climate

Abstract

Cloud Analysis plays an important role in understanding the climate changes which will be helpful in taking necessary mitigation policies. This work mainly aims to provide a novel framework for tracking as well as extracting characteristics of multiple cloud clusters by combining dense and sparse motion estimation techniques. The dense optical flow (Classic-Nonlocal) method estimates intra-cloud motion accurately from low contrast images in the presence of large motion. The sparse or feature (Region Overlap)-based estimation technique utilize the computed dense motion field to robustly estimate inter-cloud motion from consecutive images in the presence of cloud crossing, splitting, and merging scenario’s. The proposed framework is also robust in handling illumination effects as well as poor signal quality due to atmospheric noises. A quantitative evaluation of the proposed framework on a synthetic fluid image sequence shows a better performance over other existing methods which reveals the applicability of classic-NL technique on cloud images. Experiments on half hourly infrared image sequence from Kalpana-1 Geostationary satellite have been performed and results show closest match to the actual track data.