Abstract:
Interferometric Synthetic Aperture Radar (InSAR) has proved its efficiency for displacement
monitoring in urban areas. However, the large volume of data generated by this technology turns
the retrieval of information useful for structure monitoring into a big data problem. In this study, a
new tool (SARClust) to analyze InSAR displacement time series is proposed. The tool performs the
clustering of persistent scatterers (PSs) based on dissimilarities between their displacement time series
evaluated through dynamic time warping. This strategy leads to the formation of clusters containing
PSs with similar displacements, which can be analyzed together, reducing data dimensionality, and
facilitating the identification of displacement patterns potentially related to structural damage. A
proof of concept was performed for downtown Lisbon, Portugal, where ten distinct displacement
patterns were identified. A relationship between clusters presenting centimeter-level displacements
and buildings located on steep slopes was observed. The results were validated through visual
inspections and comparison with another tool for time series analysis. Agreement was found in
both cases. The innovation in this study is the attention brought to SARClust’s ability to (i) analyze
vertical and horizontal displacements simultaneously, using an unsupervised procedure, and (ii)
characterize PSs assisting the displacement interpretation. The main finding is the strategy to
identify signs of structure damage, even on isolated buildings, in a large amount of InSAR data. In
conclusion, SARClust is of the utmost importance to detect potential signs of structural damage in
InSAR displacement time series, supporting structure safety experts in more efficient and sustainable
monitoring tasks.
