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首页> 外文期刊>Journal of Geodesy >Measurement of slow-moving along-track displacement from an efficient multiple-aperture SAR interferometry (MAI) stacking
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Measurement of slow-moving along-track displacement from an efficient multiple-aperture SAR interferometry (MAI) stacking

机译:通过有效的多孔径SAR干涉测量(MAI)叠加测量慢速沿轨道位移

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摘要

Multiple-aperture SAR interferometry (MAI) has demonstrated outstanding measurement accuracy of along-track displacement when compared to pixel-offset-tracking methods; however, measuring slow-moving (cm/year) surface displacement remains a challenge. Stacking of multi-temporal observations is a potential approach to reducing noise and increasing measurement accuracy, but it is difficult to achieve a significant improvement by applying traditional stacking methods to multi-temporal MAI interferograms. This paper proposes an efficient MAI stacking method, where multi-temporal forward- and backward-looking residual interferograms are individually stacked before the MAI interferogram is generated. We tested the performance of this method using ENVISAT data from KA lauea Volcano, Hawai'i, where displacement on the order of several centimeters per year is common. By comparing results from the proposed stacking methods with displacements from GPS data, we documented measurement accuracies of about 1.03 and 1.07 cm/year for the descending and ascending tracks, respectively-an improvement of about a factor of two when compared with that from the conventional stacking approach. Three-dimensional surface-displacement maps can be constructed by combining stacked InSAR and MAI observations, which will contribute to a better understanding of a variety of geological phenomena.
机译:与像素偏移跟踪方法相比,多孔径SAR干涉测量法(MAI)已证明了沿跟踪位移的出色测量精度;然而,测量缓慢移动(厘米/年)的表面位移仍然是一个挑战。多时相观测的叠加是减少噪声和提高测量精度的一种潜在方法,但是通过将传统的叠加方法应用于多时相MAI干涉图很难实现显着的改进。本文提出了一种有效的MAI叠加方法,该方法可以在生成MAI干涉图之前将多时间的前向和后向残留干涉图单独堆叠。我们使用来自夏威夷《 KA 劳埃阿火山》的ENVISAT数据测试了该方法的性能,该地区每年的位移量约为几厘米。通过比较所提出的叠加方法的结果和GPS数据的位移,我们记录了下降和上升轨道的测量精度分别为1.03和1.07 cm / year-与传统方法相比,测量精度提高了约两倍。堆叠方法。可以通过组合堆叠的InSAR和MAI观测值来构建三维表面位移图,这将有助于更好地理解各种地质现象。

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