This paper illustrates the first wide area study over Tianjin suburbs of China using persistent scatterers interferometry (PSI) technique for ground subsidence monitoring via high-resolution TerraSAR-X (TSX) SAR data. The deformation rate and the displacement history of the subsiding areas from April 29, 2009 to November 11, 2011 have been reconstructed. The results demonstrate the capability of applying PSI technique to high-resolution SAR time series imageries for monitoring of the subsidence of multiple towns and large-scale man-made linear features (LMLFs) such as railways, highways, and power lines. The comprehensive uncertainties were analyzed between PSI results and ground-leveling data whose densities are very different in both spatial and temporal domains. The overall results show a good agreement with each other. We also found that the underground water extraction and lithological character are the two important potential explanations to the location and shape of the subsiding centers.

When synthetic aperture radar interferometry (InSAR) technology is applied in the monitoring of land subsidence, the sensor band plays an important role. An X-band SAR system as TerraSAR-X (TSX) provides high resolution and short revisit time, but it has no capability of global coverage. On the other side, an L-band sensor as Advanced Land Observing Satellite-Phased Array L-band Synthetic Aperture Radar (ALOS-PALSAR) has global coverage and it produces highly coherent interferograms, but it provides much less details in time and space. The characteristics of these two satellites from different bands can be regarded as complementary. In this paper, we firstly present a possible strategy for X-band optimized acquisition planning combining with L-band. More importantly, we also present the multi-temporal InSAR (MT-InSAR) analysis results from 23 ALOS-PALSAR images and 37 TSX data, which show the complementarity of L- and X-band allows measuring deformations both in urban and non-urban areas. Furthermore, the validation between MT-INSAR and leveling/GPS has been carried out. The combination analysis of L- and X-band MT-InSAR results effectively avoids the limitation of X-band, providing a way to define the shape and the borderline of subsiding center and helps us to understand the subsidence mechanism. Finally, the geological interpretation of the detected subsidence center is given.

Oil spill detection plays an important role in marine environment protection. Quad-polarimetric Synthetic Aperture Radar (SAR) has been proved to have great potential for this task, and different SAR polarimetric features have the advantages to recognize oil spill areas from other look-alikes. In this paper we proposed an oil spill detection method based on convolutional neural network (CNN) and Simple Linear Iterative Clustering (SLIC) superpixel. Experiments were conducted on three Single Look Complex (SLC) quad-polarimetric SAR images obtained by Radarsat-2 and Spaceborne Imaging Radar-C/X-Band Synthetic Aperture Radar (SIR-C/X-SAR). Several groups of polarized parameters, including H/A/Alpha decomposition, Single-Bounce Eigenvalue Relative Difference (SERD), correlation coefficients, conformity coefficients, Freeman 3-component decomposition, Yamaguchi 4-component decomposition were extracted as feature sets. Among all considered polarimetric features, Yamaguchi parameters achieved the highest performance with total Mean Intersection over Union (MIoU) of 90.5%. It is proved that the SLIC superpixel method significantly improved the oil spill classification accuracy on all the polarimetric feature sets. The classification accuracy of all kinds of targets types were improved, and the largest increase on mean MIoU of all features sets was on emulsions by 21.9%.

Synthetic Aperture Radar Interferometry (InSAR), widely applied for the monitoring of land subsidence, has the advantage of high accuracy and wide coverage. High-resolution SAR data offers a chance to reveal impressive details of large-scale man-made linear features (LMLFs) with Multi-temporal InSAR (MT-InSAR) analysis. Despite these advantages, research validating high-resolution MT-InSAR results along high-speed railways with high spatial and temporal density leveling data is limited. This paper explored the monitoring ability of high-resolution MT-InSAR in an experiment along Jingjin Inter-City Railway, located in Tianjin, China. Validation between these MT-InSAR results and a high spatial/temporal density leveling measurement was conducted. A total of 37 TSX images spanning half a year were processed for MT-InSAR analysis. The distance between two consecutive leveling points is 60 m along Jingjin Inter-City railway and the time interval of the study was about one month. The Root Mean Square Error (RMSE) index of average subsidence rate comparison between MT-InSAR results and leveling data was 3.28 mm/yr, with 34 points, and that of the displacement comparison was 2.90 mm with 464 valid observations. The experimental results along Jingjin Inter-City railway showed a high correlation between these two distinct measurements. These analyses show that millimeter accuracy can be achieved with MT-InSAR analysis when monitoring subsidence along a high-speed railway. We discuss the possible reason for the subsiding center, and the characteristics of both leveling and MT-InSAR results. We propose further planning for the monitoring of subsidence over LMLFs.

Investigation of subsidence situations is important for the design of railway lines. Traditional geological investigations are expensive. Multi-temporal InSAR (MT-InSAR) analysis has shown great potential for detecting subsidence areas along large-scale manmade linear features (LMLFs). The disadvantage of applying high-resolution SAR data such as that from TerraSAR-X and COSMO-SkyMed data is the high cost. By contrast, the free and open data from Sentinel-1 A/B offer an unprecedented opportunity for InSAR applications. Despite these advantages, research investigating the potential for monitoring subsidence along LMSFs using multi-temporal Sentinel-1 InSAR data at the design stage of LMSFs is limited. This paper explores the monitoring ability of MT-InSAR analysis with Sentinel-1 A/B multi-temporal data in an experiment along the planned Jakarta-Bandung high-speed railway in Bandung, Indonesia. Subsidence patterns along the route of the planned high-speed railway were analyzed in detail. By means of validation using 12 leveling points, the root mean square error (RMSE) of the average displacement velocity that was obtained from the MT-InSAR results was found to be 10.31 mm/yr. This paper demonstrates the potential of Sentinel-1 multi-temporal INSAR data to detect potential subsidence areas along planned LMLFs (e.g., the planned high-speed railway that is the focus of this study), serving as a reference for further planning of monitoring subsidence over planned LMLFs.