Adaptive Bilateral-Total-Variation Regularization Algorithm for Enhancing HY2-SCAT Data

The spaceborne scatterometer is an active nonimaging radar system that is commonly used to measure the direction and speed of wind near the ocean surface. However, the typical resolution of the current spaceborne scatterometers is 25–50 km, which limits their applicability in scenarios requiring higher resolution requirement. In this article, an adaptive bilateral-total-variation regularization algorithm with Lorentzian norm (LABTV) is proposed using HaiYang-2 Scatterometer (HY2-SCAT) data. It introduces adaptive weight coefficients based on the bilateral total variation, which suppresses the noise effectively while maintaining the texture details of the images. Moreover, the Lorentzian norm further improves the performance of the reconstruction algorithm. To investigate its performance in resolution enhancement and the resulting accuracy, the proposed reconstruction algorithm is implemented using both simulated and actual HY2-SCAT measurements. Compared with some existing resolution enhancement algorithms, the proposed algorithm can achieve a comparable resolution enhancement after enhancing two times to an original-resolution pixel size of 25 km, with the root-mean-square error (RMSE) of 1.812 dB, the peak signal-to-noise ratio (PSNR) of 26.797 dB, the structural similarity (SSIM) of 0.974, and the coefficient of determination (
$R^{2}$
) of 0.967, for horizontally polarized transmitted and received (HH-pol) data, and a comparable resolution enhancement with RMSE of 1.788 dB, PSNR of 26.991 dB, SSIM of 0.976, and
$R^{2}$
of 0.962, for vertically polarized transmitted and received (VV-pol) data. Furthermore, HY2-SCAT images with a low-resolution pixel size of 25 km were enhanced four times to a high-resolution pixel size of 6.25 km. The technique is also validated using Scatterometer Satellite-1 (SCATSAT-1) data after four times enhancement to a high-resolution pixel size of 4.45 km.