Abstract
The satellite images in Asia are produced every hour by Kochi University, Japan (URL http://weather.is.kochi-u.ac.jp/SE/00Latest.jpg). They show the development of cloud or storm movement. The sequence of satellite images can be combined to show animation easily but it is shown only from the top-view. In this paper, we propose a method to condition the 2D satellite images to be viewed from any perspective angle. The cloud or storm regions are analyzed, segmented and reconstructed to 3D cloud or storm based on the gray intensity of cloud properties. The result from reconstruction can be used for a warning system in the risky area. Typhoon Damrey (September 25 - 27, 2005) and typhoon Kaitak (October 29 - November 1, 2005) are shown as a case study of this paper. Other satellite images can be conditioned by using this approach as well.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Dudhia, J.: A nonhydrostatic version of the Penn State-NCAR mesoscale model: Validation test and simulation of an Atlantic cyclone and cold front, Mon. Wea. Rev., 121, 1493–1513 (1993)
Grell, G., Dudhia, J., and Stauffer, D.: A description of the fifth generation Penn State/NCAR Mesoscale Model. NCAR Tech. Note NCAR/TN-398 + STR, (1994)
Griffin, M. K., Hsu, S. M., Burke, H. K., and Snow, J. W.: Characterization and delineation of plumes, clouds and fires in hyperspectral images, in Proc. 2000 IEEE International Geoscience and Remote Sensing Symposium, II, In: Stein, T. I. (ed.) Piscataway: IEEE, 809–812 (2000)
Hetzheim, H.: Characterisation of clouds and their heights by texture analysis of multi-spectral stereo images, in Proc. 2000 IEEE International Geoscience and Remote Sensing Symposium, V, In: Stein, T. I. (ed.) Piscataway: IEEE, 1798–1800 (2000)
Hong, Y., Hsu, K., Gao, X., Sorooshian, S.: Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Network - Cloud Classification System, Journal of Applied Meteorology, 43, No. 12, 1834-1853, (2005)
Kubo, M., Koshinaka, H., Muramoto, K.: Extraction of clouds in the Antartic using wavelet analysis, in Proc. 2000 IEEE International Geoscience and Remote Sensing Symposium, V, In: Stein, T. I. (ed.) Piscataway: IEEE, 2170–2172 (2000)
Mukherjee, D. P., Acton, S. T.: Cloud tracking by scale space classification. IEEE Trans. Geosci. Rem. Sens., GE-40, No.2, 405–415 (2002)
Tian, B., Shaikh, M. A., Azimi-Sadjadi, M. R., Von der Haar, T. H., Reinke, D. L.: A Study of cloud classification with neural networks using spectral and textural features, IEEE Trans. Neural Networks, 10, 138–151 (1999)
Visa, A., Iivarinen, J., Valkealahti, K., Simula, O.: Neural network based cloud classifier, Proc. International Conference on Artificial Neural Networks, ICANN’95 (1995)
Welch, R. M., et al.: Cloud filed classification based upon high spatial resolution textural feature (I): Gray level cooccurrence matrix approach, J. Geophys. Res., 93, 12663–12681 (1988)
Yang, Z., Wood, G., O’Reilly, J. E.: Cloud detection in sea surface temperature images by combining data from NOAA polar-orbiting and geostationary satellites, in Proc. 2000 IEEE International Geoscience and Remote Sensing Symposium, V, In: Stein, T. I. (ed.) Piscataway: IEEE, 1817–1820 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chuai-Aree, S., Jäger, W., Bock, H.G., Krömker, S., Kanbua, W., Siripant, S. (2008). 3D Cloud and Storm Reconstruction from Satellite Image. In: Bock, H.G., Kostina, E., Phu, H.X., Rannacher, R. (eds) Modeling, Simulation and Optimization of Complex Processes. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79409-7_12
Download citation
DOI: https://doi.org/10.1007/978-3-540-79409-7_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-79408-0
Online ISBN: 978-3-540-79409-7
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)