基于注意力网络的地基SAR时序差分相位分类方法

地基合成孔径雷达(Ground Based Synthetic Aperture Radar, GBSAR)具备形变测量精度高、可大面积非接触监测、可全天候监测的优势，是对露天矿山工作帮及排土场进行亚毫米级形变监测的主要技术手段之一。边坡形变监测过程中出现的多径效应造成的差分相位变化会被错误识别为形变，而形变结果的准确性又是触发预警流程的重要依据。针对识别形变精度低的问题，本文开展了差分干涉相位时序特征表达方法研究，并以此为基础提出了一种基于注意力网络模型的地基SAR时序差分相位分类方法，以相位变化趋势与区域范围作为依据来区分突变区域和缓变区域，通过模型预测出真实形变分布。经过实验结果证明，注意力网络模型可以较为准确的提取出形变分布，有效减少多径效应造成的误差干扰。 

Time series differential phase classification of Ground-based SAR based on attention network

Ground based synthetic aperture radar (GBSAR) has the advantages of high deformation measurement accuracy, large area non-contact monitoring and all-weather monitoring. It is one of the main technical means of submillimeter deformation monitoring for working slope and dump of open pit mine. The differential phase change caused by multipath effect in the process of slope deformation monitoring will be wrongly identified as deformation, and the accuracy of deformation results is an important basis for triggering the early warning process. Aiming at the problem of low accuracy of deformation recognition, this paper studies the expression method of differential interferometric phase sequence features, and proposes a time-series differential phase classification method for ground-based SAR Based on attention network model. Based on the phase change trend and regional range, the abrupt change region and the gradual change region are distinguished, and the real deformation distribution is predicted by the model. The experimental results show that the attention network model can accurately extract the deformation distribution, and effectively reduce the error interference caused by multipath effect.