空域滤波器去除SAR图像散斑噪声的性能比较分析

Ranjith Kumar Painam, M. Suchetha
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引用次数: 0

摘要

在合成孔径雷达(SAR)图像中,散斑噪声是常见的问题,需要对SAR数据进行相干处理。乘法噪声是散斑的另一个名称。本文的目的是比较几种降低散斑噪声的方法。这些技术将用于展示多年来发展起来的趋势和许多不同的方法。本文讨论了各种自适应空间域滤波器的技术方面,并总结了它们在去除SAR图像斑点噪声中的应用。ENL、SSI和SSIM是已经进行了定量和定性分析的性能参数。结果表明,不同窗口大小的自适应滤波器可以有效地消除SAR图像中的散斑,抑制噪声效果更好。它可能会被增强,以结合几种机器学习技术来优化结果,以提高各种性能参数。实验结果表明,在抑制散斑噪声的同时,结构细节得到了较好的保留。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparative Performance Analysis of Spatial Domain Filters for Removing Speckle Noise in SAR images
In synthetic aperture radar (SAR) images, speckle noise is common, and SAR data is handled coherently. Multiplicative noise is another name for speckle. The purpose of this paper is to compare several approaches for reducing speckle noise. These techniques will be used to demonstrate trends and numerous different approaches that have evolved over the years. The technical aspects of the various adaptive spatial domain filters were discussed in this paper, and they were summarised for use in removing speckle noise from SAR images. ENL, SSI, and SSIM are the performance parameters that have been quantitatively and qualitatively analysed. It indicates that the adaptive filters with varied window sizes can be used to eliminate speckle and that noise suppression is more effective in SAR images. It may be enhanced to incorporate several machine learning techniques to optimise the result in order to improve various performance parameters. The experimental results show that the structural details are better preserved while speckle noise is suppressed.
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