## 5 Satellite Remote Sensing Techniques for Infrastructure Monitoring
### Stand-Alone Applications of Satellite Remote Sensing for Infrastructure Monitoring
#### 5.3.4 Railways  
Railway systems consist of interconnected infrastructure, including bridges and tunnels, where individual elements not working at full capacity across their service life can affect the functionality of the entire system. The use of MT-InSAR methods has increased in the last few years due to their ability to collect information with a millimetre-level accuracy. Successful applications of the PSI technique in railway monitoring are reported in Table 2. Amongst the most significant studies in this area, [PERSON] et al. [109] proposed a probabilistic method for InSAR time series post-processing to efficiently analyse the data and detect railway structural instabilities. The proposed approach was validated on the entire railway network of the Netherlands, using 213 Radarsat-2 acquisitions between 2010 and 2015. More than 3000 km of railway sections were analysed, leading to the first satellite-based nationwide application for railway monitoring. [PERSON] et al. [110] conducted a high-resolution MT-InSAR survey to monitor the Jingjin Inter-City Railway, in Tianjin (China), and validated their results with levelling measurements of high spatial/temporal density. The outcomes show a high correlation between the measurements collected with the two different methods. A total of 37 TerraSAR-X images covering a 6-month period were processed by the MT-InSAR analysis. The distance between two consecutive levelling points was 60 m along the railway, with these data being collected every month from August 2009 to January 2011. The Root Mean Square Error (RMSE) index resulting from the comparison between the average subsidence velocity of motion, detected by MT-InSAR and levelling surveys, was 3.28 mm/yr, with 34 points. Furthermore, the RMSE index from the displacements by MT-InSAR and levelling was relatively low, i.e., 2.90 mm for 464 valid observations. These analyses show that a millimetre-level accuracy can be achieved though the MT-InSAR analysis when monitoring subsidence on a high-speed railway. [PERSON] et al. [111] developed an improved persistent scatterer InSAR approach capable of retrieving and monitoring the thermal expansion effects in railways with an increased number of point-like targets (PTs). Velocity of subsidence, gradient and thermal dilation were implemented to identify the hazard risk associated with three railways selected as case studies. The proposed strategy combines the SAR amplitude, the interferometric phase and the spatial information of railway structures to maximise the number of PTs. The approach was validated using ENVISAT ASAR and TerraSAR-X (TSX) data. It is important to emphasise that maintenance of railway bridges and their structural components, such as piers and shoulders, and the monitoring of their static and dynamic response are crucial tasks. Hence, the function of detecting differential deformations along the tracks makes the PSI methods very effective for safety and structural stability monitoring of infrastructure. New Paradigms in Transport Infrastructure Monitoring: Combined Applications and Data Integration Studies of Satellite Remote Sensing and NDTs  
The PSI technique can measure deformations at the location of the PSs. Hence, historically, their use has not been suitable for areas with high temporal decorrelation, e.g., vegetated areas or areas subject to rapid urbanisation [14; 15]. These areas are characterised by the absence of stable points or reference infrastructure. Novel approaches (i.e., the distributed scatterers) can solve these problems in complex scenarios [71; 72]. Another focus point is related to the medium-resolution associated with the frequency of the SAR sensors, which can make it difficult to allocate the PS to the actual object on the ground surface, or to the relevant structural element, especially in urbanised areas. Furthermore, the analysis of medium-resolution SAR data is crucial for the systematic monitoring of infrastructure at the network level as well as for territorial analyses.