Abstract

In remote sensing, high-quality image data is crucial for effective analysis and interpretation. This study focuses on analyzing the impact of image quality by applying various frequency-domain filters like Gaussian, Butterworth, Chebyshev, Ideal, Elliptic, Laplacian, Extended Adaptive Wiener, Logarithmic and Homomorphic filter on the T3 components of SAR imagery. A quantitative analysis of image quality was carried out using metrics such as the Coefficient of Variation (CV), Signal-to-Noise Ratio (SNR), Equivalent Number of Looks (ENL), Structural Similarity Index Measure (SSIM), and Peak Signal-to-Noise Ratio (PSNR). Compared to the traditionally used Lee Refined filter, the Extended Adaptive Wiener filter demonstrated improved SNR, PSNR, and SSIM, with only slight compromises in CV and ENL. While the Lee Refined filter maintained balanced performance, other frequency-domain filters tended to either over-smooth (e.g., Butterworth, Homomorphic) or underperform (e.g., Chebyshev, Elliptic, Gaussian, Ideal, Logarithmic). These findings highlight the Extended Adaptive Wiener filter as a promising approach for speckle reduction in PolSAR data, supporting improved clarity and structural preservation in remote sensing applications.

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