Paper
30 October 2009 Typhoon cloud system segmentation with multichannel images using vector-valued Chan-Vese model
Kun Wei, Yuanxiang Li, Zhongliang Jing, Chunxiang Shi
Author Affiliations +
Proceedings Volume 7494, MIPPR 2009: Multispectral Image Acquisition and Processing; 749413 (2009) https://doi.org/10.1117/12.833129
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China
Abstract
Cloud segmentation is an important step and a very difficult problem in typhoon image processing. There are many works on cloud image segmentation, but few are carried out on typhoon primary cloud system (galaxy) segmentation. Typhoon satellite images are always multiple channels whose properties are very different, so that the appearances of these channels are different as well. In order to segment out primary cloud systems accurately, multiple channel images are employed in this paper. The image data is from MERSI (short for MEdium Resolution Spectral Imager) of Chinese FY- 3A meteorological satellite launched on March, 2008. The scalar multiphase Chan-Vese (CV) model is extended for the vector-valued images, so as to partition out typhoon cloud systems. The experiment results show that the multi-channel segmentation is more accurate, more complete and more effective than that of usually using only one image, with multiple channel images being treated as a vector one input into the CV model. The multi-channel segmentation integrates the distribution information of cloud systems in all channels, so information fusion of multiple channels are realized when segmenting.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Kun Wei, Yuanxiang Li, Zhongliang Jing, and Chunxiang Shi "Typhoon cloud system segmentation with multichannel images using vector-valued Chan-Vese model", Proc. SPIE 7494, MIPPR 2009: Multispectral Image Acquisition and Processing, 749413 (30 October 2009); https://doi.org/10.1117/12.833129
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CITATIONS
Cited by 2 scholarly publications.
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KEYWORDS
Image segmentation

Clouds

Systems modeling

Image fusion

Satellites

Meteorological satellites

Satellite imaging

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