测绘通报 ›› 2021, Vol. 0 ›› Issue (5): 68-72.doi: 10.13474/j.cnki.11-2246.2021.0144

• 学术研究 • 上一篇    下一篇

基于分时散射目标的非城区地形PS-InSAR监测

赵中枢1, 张红峰2   

  1. 1. 辽宁对外经贸学院, 辽宁 大连 116052;
    2. 华北理工大学矿业工程学院, 河北 唐山 063210
  • 收稿日期:2020-09-21 修回日期:2021-03-16 发布日期:2021-05-28
  • 通讯作者: 张红峰。E-mail:zhang_hf1@163.com
  • 作者简介:赵中枢(1978-),男,硕士,副教授,主要研究方向为大数据及网络安全技术、数字图像处理。E-mail:Zhao_zs110@sian.com
  • 基金资助:
    国家自然科学基金(41579072)

Non-urban surface deformation monitoring based on improved PS-InSAR

ZHAO Zhongshu1, ZHANG Hongfeng2   

  1. 1. Liaoning University of International Business and Economics, Dalian 116052, China;
    2. College of Mining Engineering, North China University of Science and Technology, Tangshan 063210, China
  • Received:2020-09-21 Revised:2021-03-16 Published:2021-05-28

摘要: 永久散射体(PS)在非城区的分布密度通常难以满足PS-InSAR技术的地形监测需求,导致PS-InSAR监测误差较大,而非城区通常存在一定时段内的散射目标,即分时散射目标,为此,本文提出基于分时散射目标的改进PS-InSAR算法。首先采用通过边缘保持EMD算法对SAR影像干涉对进行降噪;然后以双层K-means聚类提取非城区的分时散射目标候选集,并通过可信概率提取可靠的分时散射目标;最后通过组内加权参数迭代和组间等权融合,计算监测区的地表形变。试验结果表明,提取的分时散射目标与同位置PS点具有相近的分布特性和变化趋势,较大地提高了非城区目标点的分布密度,提高了非城区地表形变监测的精度。

关键词: 非城区地表形变监测, 改进PS-InSAR测量, 分时散射目标, 改进EMD干涉图降噪, 可信概率目标提取

Abstract: To solve the problem of large monitoring errors caused by insufficient distribution of permanent scatterers (PS), when PS-InSAR is applied for non-urban ground deformation monitoring, an improved PS-InSAR algorithm based on partial time scatterers is proposed. Based on the analysis of a large number of partial time scatterers in non-urban areas, the partial time scatterers are extracted through the combination of dual threshold and credible probability after the image is denoised by improved EMD algorithm. And then, the deformation rate is calculated through the iterative estimation of the different parameters and the phase separation of the partial time scatterer, obtaining the surface deformation of the monitoring area. Experimental results show that the extracted partial time scatterers basically cove the PS points extracted by the traditional PS-InSAR algorithm and maintains their spatial distribution characteristics and the timing change trend. The spatial distribution density required for surface deformation monitoring in non-urban areas is greatly improved. Improved the accuracy of non-urban surface deformation monitoring, which verifies the effectiveness of the algorithm.

Key words: non-urban surface deformation monitoring, improved PS-InSAR, partial time scatterers, noise reduction of improved EMD decomposition, credible probability target extraction

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