RESUMO
Current precise geometric correction of Geostationary Ocean Color Imager (GOCI) image slots is performed by shoreline matching. However, it is troublesome to handle slots with few or no shorelines, or slots covered by clouds. Geometric correction by frequency matching has been proposed to handle these slots. In this paper, we further extend previous research on frequency matching by comparing the performance of three frequency domain matching methods: phase correlation, gradient correlation, and orientation correlation. We compared the performance of each matching technique in terms of match success rate and geometric accuracy. We concluded that the three frequency domain matching method with peak search range limits was comparable to geometric correction performance with shoreline matching. The proposed method handles translation only, and assumes that rotation has been corrected. We need to do further work on how to handle rotation by frequency matching.
RESUMO
Geometric correction is fundamental in producing high quality satellite data products. However, the geometric correction for ocean color sensors, e.g., Geostationary Ocean Color Imager (GOCI), is challenging because the traditional method based on ground control points (GCPs) cannot be applied when the shoreline is absent. In this study, we develop a hybrid geometric correction method, which applies shoreline matching and frequency matching on slots with shorelines and without shorelines, respectively. Frequency matching has been proposed to estimate the relative orientation between GOCI slots without a shoreline. In this paper, we extend our earlier research for absolute orientation and geometric correction by combining frequency matching results with shoreline matching ones. The proposed method consists of four parts: Initial sensor modeling of slots without shorelines, precise sensor modeling through shoreline matching, relative orientation modeling by frequency matching, and generation of geometric correction results using a combination of the two matching procedures. Initial sensor modeling uses the sensor model equation for GOCI and metadata in order to remove geometric distortion due to the Earth's rotation and curvature in the slots without shorelines. Precise sensor modeling is performed with shoreline matching and random sample consensus (RANSAC) in the slots with shorelines. Frequency matching computes position shifts for slots without shorelines with respect to the precisely corrected slots with shorelines. GOCI Level 1B scenes are generated by combining the results from shoreline matching and frequency matching. We analyzed the accuracy of shoreline matching alone against that of the combination of shoreline matching and frequency matching. Both methods yielded a similar accuracy of 1.2 km, which supports the idea that frequency matching can replace traditional shoreline matching for slots without visible shorelines.
