Multi-temporal interferometric synthetic apertureradar (MT-InSAR) is used for many applications in earthobservation. Most MT-InSAR methods select scatterers with highcoherence throughout the entire time series. However, as timeseries lengthen, inevitable changes in surface scattering leadto decorrelation, which systematically decreases the number ofcoherent scatterers. Here, we propose a novel method to detectand process temporary coherent scatterers (TCS) by subsequentlyanalyzing the amplitude and the interferometric phase. Twohypothesis tests are developed for amplitude analysis in order toidentify the moments of appearing and/or disappearing coherentscatterers. Based on the amplitude analysis, the parametersof interest are then estimated using the interferometric phase.An optimized adaptive temporal subset approach is proposed toimprove the precision of the estimated parameters. If the scatter-ers are not evenly distributed over the area, a secondary (support)network is designed to improve the spatial point distribution.The main advantage of this method is the reliable extraction ofa subset of time series without using any contextual information.Experimental results show that the TCSs significantly increasethe number of observations for displacement monitoring andimprove the change detection capability in urban constructionareas.
|Number of pages||13|
|Journal||IEEE Transactions on Geoscience and Remote Sensing|
|Publication status||Published - 2019|
- Change detection
- Rayleigh distribution
- multi-temporal InSAR
- temporary coherent scatterer