High-resolution TerraSAR-X Images Research Articles

Detecting Subsidence in Coastal Areas by Ultrashort-Baseline TCPInSAR on the Time Series of High-Resolution TerraSAR-X Images

In this paper, we present an improved approach of the multitemporal interferometric synthetic aperture radar (InSAR) for detecting land subsidence in coastal areas by using the time series of high-resolution SAR images. In particular, our algorithm extends the capability of the temporarily coherent point InSAR (TCPInSAR) technique that can be used to detect subsidence even in the case of a small number of SAR images available for a study area. The proposed approach is implemented by using the interferograms with ultrashort spatial baselines (USBs) through several procedures, including the selection of USB interferometric pairs, TCP identification, TCP networking and modeling, as well as TCP solution by a least squares estimator. As the topographic effects in coastal areas are negligible in the USB interferograms, an external digital elevation model is no longer necessary for differential processing, thus simplifying both TCP modeling and parameter estimating. The USB-based TCPInSAR algorithm has been tested with the high-resolution TerraSAR-X images acquired over Tianjin (close to Bohai Bay) in China, and validated by using the ground-based leveling measurements. The testing results indicate that the density and coverage extent of TCPs can be increased dramatically by using the proposed algorithm, and the quality of subsidence measurements derived by the USB-based TCPInSAR can be raised.

Expressway deformation mapping using high-resolution TerraSAR-X images

Monitoring deformation of linear infrastructures such as expressway and railway caused by natural processes or anthropogenic activities is a vital task to ensure the safety of human lives and properties. Interferometric Synthetic Aperture Radar (InSAR) has been widely recognized as an effective technology to carry out large-area surface deformation mapping. However, its application in linear infrastructure deformation monitoring has not been intensively studied till now. In this article, a modified Small BAseline Subset (SBAS) method is proposed to retrieve the deformation patterns of the expressway. In our method, only the point-like targets identified on the expressway were kept in our analysis, and two complementary subsets of interferograms were formed to better separate the signals of height error and deformation from inteferometric phase observations. We successfully applied this method with multitemporal high-resolution TerraSAR-X images to retrieve the spatial-temporal pattern of surface deformation along the Bei’an-Heihe expressway that is located in island-permafrost areas and threatened by geohazards.

Damage Detection Using High-Resolution SAR Imagery in the 2009 L'Aquila, Italy, Earthquake

Satellite remote sensing is being used to monitor disaster-affected areas for post-disaster reconnaissance and recovery. One of the special features of Synthetic Aperture Radar (SAR) is that it can operate day and night and penetrate the cloud cover because of which it is being widely used in emergency situations. Building damage detection for the 6 April 2009 L'Aquila, Italy, earthquake was conducted using high-resolution TerraSAR-X images obtained before and after the event. The correlation coefficient and the difference of backscatter coefficients of the pre- and post-event images were calculated in a similar way as Matsuoka and Yamazaki (2004) . The threshold value of the correlation coefficient was suggested and used in detecting building damage. The results were compared with ground truth data and a post-event optical image. Based on the study, building damage could be observed in an urban setting of L'Aquila with overall accuracy of 89.8% and Kappa coefficient of 0.45.

An Improved Multi-Temporal Insar Method for Increasing Spatial Resolution of Surface Deformation Measurements

Abstract. The multi-temporal interferometric synthetic aperture radar (InSAR) technology has proven very useful in extracting surface deformation with time series of SAR images over a study area. To increase spatial resolution of deformation information, this paper presents an improved multi-temporal InSAR (MTI) method by tracking both the point-like targets (PTs) and the distributed targets (DTs) with temporal steadiness of radar backscattering. The valid pixels corresponding to PTs and DTs are identified mainly by thresholding of the amplitude dispersion index (ADI) and the Pearson correlation coefficient (PCC). To efficiently reduce error propagation, a hierarchical analysis strategy is applied to extract deformation rates at the valid pixels. For the pixels with lower ADI values, the deformation rates are estimated on an optimized network by a least squared estimator and a region growing method. For the pixels with higher ADI values, they are classified into several groups by the ADI intervals, and the deformation rates are estimated through the multi-levels of processing. The nonlinear deformation values at all the valid pixels are estimated by spatiotemporally filtering and spatially integrating. The proposed MTI algorithm has been tested for subsidence detection over Tianjin in China using the 40 high resolution TerraSAR-X images acquired between 2009 and 2010, and validated by using the ground-based leveling measurements. The testing results indicate that the spatial resolution and coverage of subsidence data can be increased dramatically by the hierarchical analysis, and the accuracy in subsidence values derived from the MTI solution can reach up to a millimeter level.

ULTRASHORT-BASELINE PERSISTENT SCATTERER RADAR INTERFEROMETRY FOR SUBSIDENCE DETECTION

Abstract. This paper presents an improved approach of multi-temporal interferometric synthetic aperture radar (InSAR) for detecting land subsidence phenomena by using time series of high resolution SAR images. Our algorithm extends the capability of the temporarily coherent point (TCP) InSAR technique proposed previously to detect subsidence even in the case of a small number of SAR images available for a study area. The approach proposed in this paper is implemented by using the interferograms with ultrashort spatial baselines (USB) through several procedures, including selection of USB interferometric pairs, TCP identification, TCP networking and modeling, as well as TCP solution. As the topographic effects are negligible in the USB interferograms, an external digital elevation model is no longer necessary for differential processing, thus simplifying both TCP modeling and parameter estimating. The algorithm has been tested with the high resolution TerraSAR-X (TSX) images acquired over Tianjin (China), and validated by using the ground-based leveling measurements. The testing results indicate that the density and coverage extent of TCPs can be increased dramatically, and the quality of subsidence measurements derived by the USB-based TCPInSAR can be raised.

High Resolution TerraSAR-X Image Speckle Suppression and its Fusion with Multispectral IRS LISS –III Data for Himalayan Glacier Feature Extraction

In this study an attempt is made for studying the Himalayan glacier features using TerraSAR-X and Indian Remote Sensing Satellite, Linear Imaging Self Scanning System III (IRS LISS –III) images. New generation, synthetic aperture radar (SAR) data from TerraSAR-X (TS-X) sensor provide opportunity for glacier feature studies in Himalayan rugged terrain. Spot Light High resolution mode TS-X data give idea about glacial features which remained untraceable from other existing SAR system. However, presence of speckle noise in SAR images degrades the interpretability of the glacier features. Speckle suppression filters (Lee, Frost, Enhanced Lee, Gamma-Map) are applied on SAR intensity images. On the basis of field sight seeing and insitu observations it is observed that still features are not clear. Hence attempt has been made for fusing multitemporal multispatial speckle reduced TS-X SAR data and multispectral IRS LISS-III data for extracting the glacial features such as crevasses, exposed ice and superaglacier lakes. Principal component analysis (PCA) represents the high spectral resolution data in a linear subspace with minimum information loss. Herein, PCA based image fusion technique is adopted for this study and comparison is made between IHS fusion technique and PCA based technique for glacier studies in the Himalayan region.

Exploration of Subsidence Estimation by Persistent Scatterer InSAR on Time Series of High Resolution TerraSAR-X Images

Ground subsidence is a major concern for land use planning and engineering risk assessment. This paper explores subsidence detection by the persistent scatterer (PS) interferometric synthetic aperture radar (InSAR) technique using the multitemporal high resolution spaceborne SAR images. We first describe the mathematical models and the data reduction procedures of the PS solution. The experiments of subsidence detection are then carried out over the Jinghai County in Tianjin (China) which has been sinking due to overuse of groundwater. The time series of high resolution SAR images collected by the X-band radar sensor onboard the satellite TerraSAR-X (TSX) are utilized for the PS detection, PS networking and subsidence estimation. The experimental results demonstrate that the high resolution of TSX SAR images can dramatically increase the PSs' density and coverage extent, especially in the built-up areas. Subsidence values can be extracted on the individual objects like buildings, street lamps and manhole covers, and on the linear engineering structures like the Jinghu high-speed railway. The PS InSAR with short radar wavelength (3.1 cm) is quite sensitive to ground displacement in the radar line-of-sight direction, and the derived subsidence measurements are in good agreement with the in situ data taken by optical leveling.

Wind and wave observations off the south Pacific Coast of Mexico using TerraSAR-X imagery

High-resolution TerraSAR-X images are analysed for the first time to investigate the effects of gap winds on the sea surface off the south Pacific Coast of Mexico. StripMap and ScanSAR scenes were acquired over the Gulf of Tehuantepec on March 2008, coinciding with a strong Tehuano event with a wind speed of about 24 ms−1. Although spatial variability of wind and wave parameters derived from the image analysis was fairly consistent with model data, Synthetic Aperture Radar (SAR)-derived wind speed was generally underestimated by 33%. The wave-retrieval algorithm, contrastingly, performed well enough to show evidence of shoaling and refracting ocean swell close to the shore. Furthermore, spatial evolution of image intensity spectral peaks suggests the presence of wave energy propagating to the SW, closely aligned with the offshore winds. Although further analyses are required, it could be the first time that growing waves in fetch-limited conditions have been detected through SAR remote sensing.

Assessment of TerraSAR-X Products with a New Feature Extraction Application: Monitoring of Cylindrical Tanks

There is no doubt that retrieving observed scene features is one of the most interesting and challenging activities in all fields of remote sensing: The successful extraction of scene parameters may not only mean the success of the adopted procedure but also the success of a prediction model, an image product, a sensor project, or even an entire mission. This paper is partly concerned with this. The mission, the sensor, and the products at issue are the TerraSAR-X; the feature retrieval approach is the deterministic model-based approach already tested on E-SAR images and now in phase of improvement and testing on high-resolution TerraSAR-X images. Together with assessing the performances of TerraSAR-X products, this paper deals with a new application which, until now, has not received enough attention even if being worth of it: monitoring of big tanks in suburban or urban areas. Detailed discussion concerning the most suitable product for this kind of application is accompanied by retrieval results carried out on recently acquired TerraSAR-X images.