Highly Accurate Geometric Correction for NOAA AVHRR Data

Abstract

Since 1970, AVHRR (Advanced Very High Resolution Radiometer) on board the NOAA (National Oceanic and Atmospheric Administration) series of satellites has been an ideal observatory for daily global observation of the Earth. NOAA AVHRR data provides very useful information about ecosystems, climate, weather and water from all over the world. It is also widely used for land cover monitoring at global and continental scales. NOAA AVHRR data are presented in the image coordinate system. Frequently, however, information extracted from AVHRR data is integrated with map data or given to consumers in a map-like form. Therefore, it is necessary to transform NOAA AVHRR data from the image coordinate system into the map coordinate system. In those applications using NOAA AVHRR data, geometric correction with high accuracy plays a very important role to ensure that NOAA AVHRR data is precisely transformed from one coordinate system to another. Some geometric correction methods for NOAA AVHRR data, or NOAA images, have been proposed. The most popular methods can be divided into two types: orbital geometry model and transformation based on ground control points (GCPs). In the former, the knowledge about the characteristics of the satellite is used to build a physical model that defines the sources of error and the direction as well as the magnitude of their effects. However, this type of method is based on only nominal parameters. It takes into account only selected factors that cause geometric distortion. Variations in the altitude or attitude of the satellite are not considered because the information needed to correct caused by these variations is not generally available (Mather, 2004). The latter looks at the problem from the opposite of view. Rather than attempt to construct the physical model that produces errors, it uses an empirical method to compare the differences between the positions of GCPs, which can be identified both on the image and on the map of the same area. From the differences between the distributions of GCPs on the image and on the map, the errors can be estimated and removed (Mather, 2004). Recently, precise geometric correction method (Ono & Takagi, 2001; Takagi, 2003), which uses GCP template matching and considers elevation effect, has obtained accurate results by considering residual errors and elevation effect when acquiring the errors, 12