Radar Reflectance Model for the Extraction of Height from Shape from Shading Technique

The shape-from-shading (SFS) technique deals with the recovery of shape of an object through a gradual variation of shading encoded in the image. Most SFS approaches have assumed Lambertian surface to extract DEM from individual images. The quality of the derived DEM from radar SFS in particular, depends on the appropriate radar reflectance model, which relates the radar backscatter to the surface normal.This paper will focus on a new reflectance model for relatingthe radar SAR backscatter coefficient values to surface normal orientation. An iterative minimization SFS algorithm was implemented using this radar reflectance model to derive the height measurements.The most important key of derivation of the surface height using this model is forward and inverse Fast Fourier Transform (FFT). The model performance was evaluated on RADARSAT-1 image using both graphical and statistical analysis. Root mean square error (RMSE) and coefficient of determination (R 2 ) were used as evaluation criteria for the model performance. The model has shown good performance in reconstructing surface heights from RADARSAT-1 imagery. It gave 17.47m and 97.2% for RMSE and R 2 , respectively.