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

delete2024-01-01
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PRE
AI
L
Lilan Li
L
Lingjia Gu *
X
Xiaofeng Li
T
Tao Jiang
X
Xintong Fan
DOI:10.1109/TGRS.2024.3425888delete
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摘要

摘要

En 中文
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.
Keyword:
Adaptive bilateral-total-variation regularization algorithm with Lorentzian norm (LABTV) regularization reconstruction (RR) algorithm
adaptive weight coefficients
image reconstruction
Lorentzian norm
scatterometer
Adaptive bilateral-total-variation regularization algorithm with Lorentzian norm (LABTV) regularization reconstruction (RR) algorithm
adaptive weight coefficients
image reconstruction
Lorentzian norm
scatterometer

期刊

IEEE Transactions on Geoscience and Remote Sensing 封面图
IEEE Transactions on Geoscience and Remote Sensing
IF:
8.6
论文数:
2.1W
被引数:
10.7W

机构

J
Jilin University
学者数:
8.7W
论文数: 5.6W
被引数: 8.9K
C
chinese academy of sciences
学者数:
56.7W
论文数: 45.0W
被引数: 704
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