Incidence Angle Correction of SAR Sea Ice Data Based on Locally Linear Mapping

Abstract

Radar backscatter variations that occur because of incidence angle effects constrain the application of Scanning Synthetic Aperture Radar (ScanSAR) data for sea ice monitoring and observations. In this paper, a class-based correction is proposed for normalizing each class in ScanSAR data to a nominal incidence angle. Two tested sea ice synthetic aperture radar (SAR) data sets were acquired: a data set for the Gulf of Saint Lawrence, which was obtained by the RADARSAT-2 satellite, and a data set for the Bohai Sea, which was obtained by the ENVISAT Advanced Synthetic Aperture Radar. An unsupervised classification is performed on each image block prior to normalization, and the incidence angle range of each image block is approximately 5°. Because the distribution of the backscatter coefficients in the azimuth band is discrete and nonlinear, the class-based locally linear mapping (LLM) technique is implemented, based on the assumption that a small quantity of sorted backscatter coefficients is locally linear. This algorithm is a transplantable and easily applied method that requires limited ground data, and it is also a semiautomated technique because nearly all of its parameters can be adaptively determined during the image analysis. The results demonstrate that LLM-corrected ScanSAR images appear to have more detailed textures, and the natural signal variability in the radar data is preserved, which indicates that the LLM produces better results compared with the histogram-based-alike (HIST-alike) technique when correcting the incidence angle in the sea ice SAR data. The results of the data analysis in this paper show that the width of the azimuth band should be selected based on the extent of variation in the incidence angle, and the reference band can be calculated based on the maximum interclass distance principle. The intercomparisons also reveal that the proposed algorithm can improve the accuracy of supervised classifications.