A study of paddy monitoring system using NOAA and MOS-1 data
Preprocessings
A flow chart of the image processings in this study is shown in
fig 2. geometric correction an radiometric correction are very important in these processings.
- Geometric correction
In this section, geometric correction for AVHRR data are described. In the geometric correction for this study, there are following two problems.
- Higher geometric accuracy is required to co-register AVHRR data for clouds elimination.
- Faster processing algorithms for geometric corrections are required to process large quantity of data within a limited duration.
Following processings have been adopted to solve these problems. Faster and accurate geometric corrections were performed by three step processings. At the first step, tangential and each curvature distortions were corrected using table look up algorithm.
In the second step, geometric corrections using orbital elements were accelerated with the aid of scan and pixel functions. One dimensional 3rd and 2nd order polynomials for each function were sufficient to maintain within 1 pixel relative accuracy.
Last step is the co-registration process of images. As most of images are largely covered by clouds, cloud free areas of each image were first selected and a correlation techniques was used to determine control points. Images were then superimposed with the aid of these control points. With these techniques, co-registration of images were achieved within 1 pixel accuracy.
Radiometric distortions mainly caused by incident sun light should be eliminated. In order to eliminate radiometric distortions, sun angle correlations were first applied to geometrically correction images. However, there exists brightness differences between different date images mainly caused by atmospheric conditions. These differences were normalized by histogram normalization process using pixels which can be through to be in the same conditions.
Fig. 2 Flow chart of processing
Clouds elimination
The most primitive idea to generate cloud free image from images is that the channel 1 and 2 values of cloud parts are larger than those of cloud free area. However, the method using this idea also picks up shadows of clouds. In order to avoid this defect, thresholding was introduced to eliminate shadow areas. Thresholding caused another defect that water areas like lakes and rivers were sometimes eliminated as shadows. From these reasons. Therefore, the method using original data values could not applied directly.
In order to eliminate clouds and shadows simultaneously, the method using N.V.I. (normalized vegetation index) was used. NVI can be calculated by the following equation.
N.V.I. = (Channel 2 - channel 1) / )channel 2 + Channel 1)
The N.V.I. is then scaled as follows :
Scaled N.V.I. = 240 - (NVI + 0.05) x 350
As shown in the above equation, scaled NVI has larger values in cloud of shadow area where the difference of CH. 1 and CH 2 is relatively small. On the contrary, scaled NVI in water area has not so large value because there exist some differences between Ch. 1 and Ch. 2. Cloud free images could be generated by taking the area which have the smallest scaled NVI value.
