Spatial Filter Analysis for Speckle Reduction in Synthetic Aperture Radar Images
Keywords:
Multilooking, ENL, Sentinel SAR, Spatial Filters, Statistical AnalysisAbstract
High-resolution Earth imagery can be captured with Synthetic Aperture Radar (SAR), a powerful tool that works in a variety of lighting and weather conditions. However, speckle noise distorts SAR images, making them less interpretable and of lower quality overall. Using speckle filtering becomes essential in the SAR image processing pipeline in order to protect important elements in these images. Speckle noise reduction significantly improves the overall quality and interpretability of SAR imagery. The literature has proposed a number of methods, including frequency domain filtering, multilooking, wavelet-based filtering, and spatial filtering. Every method has unique benefits and limitations, and the best filter option depends on the use cases and sensor capabilities. The efficacy of a speckle filter is determined by how well it reduces noise while maintaining image edges and fine details. Analysing filtered imagery guarantees that important details are preserved in addition to the desired noise reduction. Standard benchmarks for assessing speckle filter performance include peak signal-to-noise ratio (PSNR), normalised root mean square error (NRMSE), mean square error (MSE), and structural similarity index (SSIM). Speckle filtering, which improves image quality and makes interpretation simpler, is still, in essence, an essential step in the processing of SAR images. Sentinel SAR imagery is used to support qualitative and quantitative analyses of spatial filters for efficient speckle reduction.
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