This paper proposes a method that estimates the position of clouds from VIS images (visible), and IR images (infrared)
of GMS (Geostationary Meteorological Satellite). In estimating the position of clouds, because the brightness value of
land and sea is lower than cloud, and the brightness value of land and sea is continually varied by altitude of sun, the
cloud area cannot be estimated by threshold processing. In this study, Variation character of brightness value is classified
in each area, and the processing method of each area is proposed based on this variation character. In land area, there is
correlation between brightness value of VIS and IR image if the area is not covered by cloud. Thus, the object domain is
estimated cloud area using the correlation between them. In sea area, due to temperature is stable, cloud area is estimated
by background subtraction method. This method was used to estimate and evaluated in the 202 GMS-5 images. The
evaluated results shown that the proposed method is more accurate than the previous method, which estimated by
threshold processing (Omi, 2003).
In recent years, NOAA images have been provided very useful information about ecosystems, climate, weather and
water from all over the world. In order to use NOAA images, they need to be transformed from image coordinate system
into map coordinate system. This paper proposes a method that corrects the errors caused by this transformation. First,
elevation values are read from GTOPO30 database and they are verified to divide data into flat and rough blocks. The
elevation errors of all blocks are then calculated based on the elevation values. After correcting elevation errors, residual
errors are specified by GCP template matching. On the flat blocks, residual errors are corrected by affine transformation;
on the rough blocks, residual errors are corrected by applying Radial Basic Function Transformation to the residual
errors of the blocks that match GCP templates. With this correction method, residual errors are corrected precisely and
the errors of interpolation process are reduced. This method was applied to correct the errors for NOAA images
receiving in Tokyo, Bangkok and Ulaanbaatar. The results proved that this is a high accurate geometric correction
method.
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