LIU Limin, GONG Huili, YU Jie, et al. Stable pointwise target detection method and small baseline subset INSAR used in beijing subsidence monitoring. [J]. Journal of Remote Sensing 20(4):643-652(2016)
LIU Limin, GONG Huili, YU Jie, et al. Stable pointwise target detection method and small baseline subset INSAR used in beijing subsidence monitoring. [J]. Journal of Remote Sensing 20(4):643-652(2016) DOI: 10.11834/jrs.20165134.
Stable pointwise target detection method and small baseline subset INSAR used in beijing subsidence monitoring
Identification of stable pointwise target is an important procedure in multi temporal multi-temporal INSAR analysis and application in monitoring regional surface deformation. The accurate identification result helps to improve the land subsidence inversion precision.Various methods for pointwise target detection have been proposed during the past two decades from different respective. The methods can be divided into some main categories according to the criteria for coherence point selection
such as amplitude dispersion index DA
signalto-Clutter ratio and phase stability. The DA method performs a time series analysis on amplitude instead of phase
and reflects the stability of series amplitude. The advantage of coherence point selection by phase stability can identify some special objects with a stable phase
which further increases the density of the stable pointwise target points
but ignores the highly scattering reflection characteristics of the coherent point. The existing methods take insufficient account of the overall features of stable pointwise targets. For ensuring stable scattering mechanism and temporal stability of pointwise target
an improved method with subaperture correlation was proposed in this paper. First
the subaperture correlation properties IHP of SAR images were obtained by spectral decomposition. Then the stability of targets is evaluated based on series two-aperture spectral coherence
by which the coherence points with high scattering could be identified and detect as PSC1.The DA threshold is utilized as the second criterion
which means any pixel in PSC1 with amplitude dispersion less than 0.4 can be determined as PSC2 Then phase stability analysis was carried out to screen out the true stable points from PSC2 with the Characteristics of high scattering mechanism and temporal stability. The experiments of stable pointwise target detection were performed using 33 high resolution SAR images collected by the Terra SAR X-band radar sensor covering Beijing. The detection results demonstrated that the improved method can detect more accurate and reliable pointwise targets than those identified by traditional methods To further confirm the effectiveness of the proposed method
the small subset INSAR technique based on the proposed coherence point detection method was adopted to retrieve the ground deformation by 40 scenes dataset acquired from 2003 to 2009 in Beijing. The vertical surface displacement rates during this period was validated by the leveling observations
with RMSE =1.36 mm/a
indicating two types of subsidence matched very well. The maximum subsidence rate of Beijing in investigated area has reached –92.25 mm/a
with an obvious uneven spatial distribution. Subaperture correlation is sensitivity to the high scattering body and can ensure stable scattering mechanism and temporal stability of pointwise target. Both coherence point detection results and the primary surface deformation proved the effectiveness of the proposed method. The deformation result during 2003–2009 has undergone severe land subsidence with high spatial aggregation characteristic
and the regional subsidence and the groundwater exploitation reveal good corresponding relationship
the more exploitation
the higher deformation rate.
关键词
子孔径分解高相干点探测短基线INSAR时空分布地下水
Keywords
Subaperture decompositionshigh coherence point detectionsmall subset INSARTemporal-spatial distributionground water
1 Institute of Remote Sensing Applications,Chinese Academy of Sciences
2 The Research Center for Remote Sensing and GIS,Dept.Geography,Beijing Normal University,Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities
3 Department of Geography and Center for Remote Sensing,Boston University,Boston MA02215,USA, 4 Chinese Center of Disease Control and Prevention