A Novel Technique for Noise Reduction in InSAR Images

This letter proposes a new technique for noise reduction applied to synthetic aperture radar interferometry. This technique involves a nonlinear filter that separates the interferogram into two components: one containing the smooth (low frequency) part and the other containing the detail (high frequency) part. The smooth part is obtained using a combination of a median filter and a smoothing filter. The detail component is obtained by subtracting the smooth component from the original signal. This detail component is filtered to remove noise and then added to the smooth component to generate the final output. Both simulated and real data are used to evaluate the performance of the proposed technique under different conditions. The experimental results show that the proposed technique outperforms most commonly used interferometric phase filters     

Full-scale structural monitoring using an integrated GPS and accelerometer system

Accurate in situ measurement of the full-scale structural responses, especially tall buildings, under severe loading conditions is an important requirement for validating their design, evaluating their construction as well as facilitating their maintenance. Traditionally such response has been measured using accelerometers. However, it is impossible to measure the static and quasi-static components of motion with acceleration sensors. An integrated system comprising of RTK-GPS and accelerometers has been developed with the objective of assessing full-scale structural responses by exploiting the complementary characteristics of GPS and accelerometer sensors. The data used in this paper were obtained from GPS and accelerometer sensors installed on a 108-m-high steel tower in Tokyo, together with other sensors such as anemometer and strain gauge. The seismic and wind-induced responses of the tower were analyzed using the Fast Fourier Transformation (FFT) and compared to results from finite element modeling (FEM). In order to study the system reliability, the correlated signals were extracted by applying a digital filtering technique. Then the filtered data sets were converted to displacement (in the case of accelerometer data) and acceleration (in the case of GPS data) through double integration and double differentiation, respectively, for the purpose of direct comparison and to further fuse data from the two different sensors. The results agree with each other very well, although the static and quasi-static components are missing from the accelerometer-derived results. The redundancy of the monitoring system therefore has been achieved.