Share Email Print

Proceedings Paper

Cloud classification by using multi-spectral GMS imagery and comparison with surface cloud observation
Author(s): Fan Yu; Hao Shao
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

In order to realize cloud classification in all-time, all the spectral information of GMS-5 satellite imagery has been exploited and made full use of in this paper. 2D~5D maximum likelihood algorithm was respectively used to experimental research on cloud classification of multi-spectral GMS imagery. In contrast with 415 surface cloud observation records in analysis region at 0800 local time, July 21,1998, if these cloud reports are strictly regarded as true, the mean accuracy of 15 kinds of 2D~5D cloud classification results is 64.9%. After the similarities and differences of satellite observation and surface cloud observation were surveyed, this paper points out that it is not completely right and reasonable that the results of cloud classification are distinguished between right and wrong absolutely according to surface cloud observation. Because the visual cloud observation from bottom to up on the ground is inevitably unilateral, the two results of different observation is sometimes hard to compare directly, contrasted the visual field observation from up to bottom of satellite. Therefore, the speciality of satellite observation must be fully noted when cloud classification is achieved by using multi-spectral satellite imagery, so in this paper the definition of distinguishing middle cloud and low cloud are put forward mainly according as brightness temperature of cloud top and make full use of multispectral information to differ thin cirrus and thick cirrus from low and middle cloud. To those samples classified as error by the criterion from surface cloud observation, it should be reappraised based on the speciality of visual field observation from up to bottom and the actual situation of satellite observation. The result reappraised to 35.1% of the "error" samples shows that 17.8% of those should be thought reasonable. The mean accuracy of 15 kinds of 2D~5D cloud classification results has been to 82.7% and the maximum accuracy is up to 87.0%, which is obtained from the 4-D maximum likelihood dynamic clustering of four wave band (IR, VIS, WV and TIR2-IR1 ) GMS imagery data. The accuracy of cloud classification also reaches to 81.4% using the other four band (IR, WV , T WV -IR1 and TIR2-IR1) imagery , especially when there is no VIS imagery at night. The final example shows on condition that multispectral information has been fully used, different spectral bands combination are utilized reasonably day and night respectively, the reasonable cloud classification will be well realized in all-time by using maximum likelihood algorithm.

Paper Details

Date Published: 11 December 2008
PDF: 9 pages
Proc. SPIE 7149, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques, and Applications II, 71490S (11 December 2008); doi: 10.1117/12.804702
Show Author Affiliations
Fan Yu, Nanjing Univ. (China)
Hao Shao, Nanjing Univ. (China)

Published in SPIE Proceedings Vol. 7149:
Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques, and Applications II
Allen M. Larar; Mervyn J. Lynch; Makoto Suzuki, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?