Detection of geophysical features in InSAR point cloud data sets using spatiotemporal models

Abstract

In this article, we introduce an approach for detecting evolving geophysical features within interferometric synthetic aperture radar (InSAR)-derived point cloud data sets. This approach is based on the availability of models describing both spatial and temporal behaviours of the geophysical features of interest. The model parameters are used to generate a multidimensional space that is then scanned with a user-defined resolution. For each point in the parameter space, a spatiotemporal template is reconstructed from the original model. This template is then used to scan the point cloud data set for regions matching the spatiotemporal behaviour.We also introduce a proportional measure where the residual for each point in the data set is compared to both the data and the template to provide a scale invariant measure of the behavioural matching. The matching is evaluated for every point in the parameter over a region of influence determined by the parameters. The resulting multidimensional space is then collapsed onto geographical coordinates to produce an overlay map identifying regions whose spatiotemporal behaviour matches the feature of interest.We tailored our approach to the detection of subsidence behaviour, indicative of the development of sinkholes, modelled as Gaussian with amplitude linearly increasing with time. We verified the validity of our model using both synthetic and actual InSAR data sets. The latter was obtained by processing imagery of a region near Wink, Texas, containing ground truth sinkhole data.We applied this framework to a 40 km × 40 km area of interest located in western Virginia and performed ground validation on a subset of the identified regions. The results show good agreement between the locations detected by our algorithm and the evidence of subsidence observed during the ground validation campaign.