Abstract
Cloud motion analysis plays a key role in analyzing the climatic changes. Recent works show that Classic-NL approach outperforms many other conventional motion analysis techniques. This paper presents an efficient approach for assimilation of satellite images using a recursive stochastic filter, Weighted Ensemble Transform Kalman Filter (WETKF), with appropriate dynamical model and image warping-based non-linear measurement model. Here, cloud motion against the occlusions, missing information, and unexpected merging and splitting of clouds has been analyzed. This will pave a way for automatic analysis of motion fields and to draw inferences about their local and global motion over several years. This paper also demonstrates efficacy and robustness of WETKF over Classic-Non-Local-based approach (Bibin Johnson J et al., International conference on computer vision and 11 image processing, 2016) [1].
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Acknowledgements
We acknowledge Meteorological and Oceanographic Satellite Data Archival Centre (MOSDAC), Space Applications Centre, ISRO, European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) for providing satellite images and INRIA for Particle Image Velocimetry (PIV) images.
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Mounika, K., Sheeba Rani, J., Subrahmanyam, G.S. (2018). Stochastic Assimilation Technique for Cloud Motion Analysis. In: Chaudhuri, B., Kankanhalli, M., Raman, B. (eds) Proceedings of 2nd International Conference on Computer Vision & Image Processing . Advances in Intelligent Systems and Computing, vol 703. Springer, Singapore. https://doi.org/10.1007/978-981-10-7895-8_9
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DOI: https://doi.org/10.1007/978-981-10-7895-8_9
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