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Benefits of blind speckle decorrelation for InSAR processing

机译:盲斑点去相关对InSAR处理的好处

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In this paper, the authors have investigated whether the noise whitening procedure developed as a preprocessing step before despeckling of detected images may be useful also in contexts where phase information is exploited. In a preliminary test set, an interferometric pair of COSMO-SkyMed StripMap images, featuring industrial buildings and vegetated areas, has been: 1) focused without Hamming window (aimed at improving the focusing of targets at the cost of introducing a spatial correlation of background noise), starting from raw data. 2) focused with Hamming window, starting from raw data; 3) preprocessed for complex noise whitening, starting from data at point 2). From the complex interferograms, coherence and interferometric phase maps have been calculated for the three cases by means of boxcar filtering. In case 1) coherence is low on vegetation and also suffers from spreading of areas characterized by strong backscattering because of the presence of high sidelobes. In case 2) points targets and buildings in general are much more defined, thanks to the sidelobe suppression achieved by Hamming filtering, but the background coherence is abnormally increased, due to the introduction of a spatial correlation. Case 3) is the most favorable because whitening operation carries out low coherence on vegetation and high coherence on buildings, where the effects of Hamming filtering are retained. An analysis of the phase field reveals that case 3) should be expedited also in terms of phase unwrapping. Thus, the whitening procedure, devised as a blind preprocessing patch of SLC data, with the goal of a better despeckling, is useful also for SAR interferometry, in which the tradeoff, dictated by the coefficient of the Hamming window, between the ideal situations of focused targets and uncorrelated speckle may be relaxed.
机译:在本文中,作者调查了在对检测到的图像进行去斑点之前,作为预处理步骤开发的噪声白化程序是否在使用相位信息的情况下是否也有用。在初步的测试集中,已对具有工业建筑和植被区域的COSMO-SkyMed StripMap图像进行了干涉对:1)在没有汉明窗的情况下进行了聚焦(旨在以引入背景空间相关性为代价来改善目标的聚焦)噪声),从原始数据开始。 2)从原始数据开始,以汉明(Hamming)窗口为焦点; 3)从点2)的数据开始进行预处理,以进行复杂的噪声白化。根据复干涉图,通过盒车滤波,针对这三种情况计算了相干和干涉相图。在情况1)中,植被的连贯性较低,并且由于存在高旁瓣,还遭受了以强烈反向散射为特征的区域的扩散。在情况2)中,由于通过汉明滤波实现了旁瓣抑制,通常更明确地定义了点目标和建筑物,但是由于引入了空间相关性,背景相干性异常增加。情况3)是最有利的,因为增白操作对植被具有较低的连贯性,而对建筑物具有较高的连贯性,并且保留了汉明滤镜的效果。对相场的分析表明,在相展开方面,也应加快情况3)。因此,以更好地去除斑点为目标,被设计为SLC数据的盲目预处理补丁的增白程序对于SAR干涉测量也很有用,在该干涉中,由汉明窗系数决定的理想状态之间的权衡取舍。聚焦的目标和不相关的斑点可能会放松。

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