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Adaptive sidelobe reduction in SAR and INSAR COSMO-SkyMed image processing

机译:SAR和INSAR COSMO-SKEDMED图像处理的自适应侧瓣减少

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The main lobe and the side lobes of strong scatterers are sometimes clearly visible in SAR images. Sidelobe reduction is of particular importance when imaging scenes contain objects such as ships and buildings having very large radar cross sections. Amplitude weighting is usually used to suppress sidelobes of the images at the expense of broadening of mainlobe, loss of resolution and degradation of SAR images. The Spatial Variant Apodization (SVA) is an Adaptive SideLobe Reduction (ASLR) technique that provides high effective suppression of sidelobes without broadening mainlobe. In this paper, we apply SVA to process COSMO-SkyMed (CSK) StripMap and Spotlight X-band data and compare the images with the standard products obtained via Hamming window processing. Different test sites have been selected in Italy, Argentina, California and Germany where corner reflectors are installed. Experimental results show clearly the resolution improvement (20%) while sidelobe kept to a low level when SVA processing is applied compared with Hamming windowing one. Then SVA technique is applied to Interferometric SAR image processing (INSAR) using a CSK StripMap interferometric tandem-like data pair acquired on East-California. The interferometric coherence of image pair obtained without sidelobe reduction (SCS_U) and with sidelobe reduction performed via Hamming window and via SVA are compared. High resolution interferometric products have been obtained with small variation of mean coherence when using ASLR products with respect to hamming windowed and no windowed one.
机译:在SAR图像中有时清晰可见主叶和强散射体的侧瓣。当成像场景包含诸如具有非常大的雷达横截面的船舶和建筑物之类的物体时,Sidelobe减少特别重要。幅度加权通常用于抑制图像的缺陷,以牺牲MainLobe扩大,分辨率丧失和SAR图像的降解。空间变型偏移(SVA)是一种自适应旁瓣还原(ASLR)技术,其在不展现Mainlobe的情况下提供高效抑制侧链。在本文中,我们将SVA应用于处理COSMO-SKYMED(CSK)Stribmap和Spotlight X波段数据,并将图像与通过汉明窗户处理获得的标准产品进行比较。在意大利,阿根廷,加利福尼亚州和德国安装了不同的测试网站,其中安装了角落反射器。实验结果显然显示了分辨率改善(20%),而侧链在应用SVA加工时保持在较低水平的情况下,与汉明窗口窗口相比。然后,使用在东加州获取的CSK RILLMAP干涉串联数据对,将SVA技术应用于干涉测量SAR图像处理(INSAR)。比较通过垂直减少(SCS_U)获得的图像对的干涉相干性和通过汉明窗口和通过SVA进行的侧链还原。在使用ASLR产品相对于汉明窗口和窗帘的情况下,已经获得了高分辨率干涉产品。

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