PROJECT TITLE :
Incidence Angle Correction of SAR Sea Ice Data Based on Locally Linear Mapping
Radar backscatter variations that occur as a result of of incidence angle effects constrain the applying of Scanning Synthetic Aperture Radar (ScanSAR) data for ocean ice monitoring and observations. In this paper, a class-based mostly correction is proposed for normalizing every category in ScanSAR knowledge to a nominal incidence angle. 2 tested sea ice synthetic aperture radar (SAR) knowledge sets were acquired: a data set for the Gulf of Saint Lawrence, that was obtained by the RADARSAT-2 satellite, and a data set for the Bohai Sea, which was obtained by the ENVISAT Advanced Artificial 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 five°. As a result of the distribution of the backscatter coefficients within the azimuth band is discrete and nonlinear, the category-based regionally linear mapping (LLM) technique is implemented, based on the assumption that a small quantity of sorted backscatter coefficients is domestically linear. This algorithm is a transplantable and easily applied method that requires restricted ground data, and it's additionally a semiautomated technique as a result of nearly all of its parameters will be adaptively determined throughout the image analysis. The results demonstrate that LLM-corrected ScanSAR pictures seem to own more detailed textures, and the natural signal variability within the radar knowledge is preserved, that indicates that the LLM produces higher results compared with the histogram-primarily based-alike (HIST-alike) technique when correcting the incidence angle in the ocean ice SAR knowledge. The results of the data analysis in this paper show that the width of the azimuth band ought to be selected primarily based on the extent of variation in the incidence angle, and also the reference band will be calculated primarily based on the maximum interclass distance principle. The intercomparisons also reveal that the proposed algorithm can improve the accuracy of supervised classifications.
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