Adaptive regularization method for forward looking Azimuth super-resolution of a Dual-Frequency Polarized Scatterometer

Dual-Frequency Polarized Scatterometer (DFPSCAT) is a pencil-beam rotating scatterometer which is used to measure snow water equivalence (SWE). Respecting the low azimuth resolution of its forward-looking region, an adaptive regularization deconvolution super-resolution method, based on the scatterometer echo signal model, is proposed. Compared with the classical SIR and MAP algorithms, the proposed method can better reconstruct the original signal, and has less noise amplification. The algorithm processing accuracy with different K_pc is also studied, and the results show that when the value of K_pc is less than 0.1, nearly the entire restored data can satisfy the requirement of 0.5dB accuracy.