جستجوی مقالات مرتبط با کلیدواژه « interferometry » در نشریات گروه « عمران »

Introduction Persistent Scatterer Interferometry (PSI) is a technique to detect and analysis of a network of coherent pixels referred as Permanent/Persistent scatterer (PS) which are stable throughout time-series of SAR images. This technique has been applied to monitor and measure phenomena such as earth subsidence fault movements and earthquake volcanic activity and other geological and environmental studies. In all PSI techniques, the processing is carried out only on the PS pixels. Therefore, high density and phase quality of these pixels are the most effective factors on the results of these techniques. The main challenge of this technique is to detect the coherent pixels over non-urban areas which suffer from the temporal decorrelation. Nowdays and with the development of polarimetric SAR sensors, polarimetric radar data are available. Polarimetric data consist of several conventional SAR acquisitions, usually addressed as channels. Each channel in a PolSAR acquisition is sensitive to different geometric characteristics of the scene. This additional redundancy over the scene may allow to increase both quality and density of the PS pixels. Therefore, the combination of polarimetry and interferometry enables to improve the effectiveness of PSI techniques, especially in non-urban areas. In this paper, we employ a method to improve the conventional PSInSAR algorithm for detecting PSC by using polarimetric optimization method on dual-pol SAR data. The improvement of this research is based on minimizing ADI criterion by means of an Exhaustive Search Polarimetric Optimization method to increase the number of PSCs. Materials & Methods 2.1 Dataset Description   The proposed method is tested using a dataset of 17 dual-pol SAR data (VV/VH) acquired by Sentinel1-A satellite March 2017 and October 2017.    2.2 Polarimetric SAR Interferometry   A general formulation for polarimetric SAR interferometry was proposed in (Cloude & Papathanassiou, 1997). The scattering matrix S represents the polarimetric information associated to each pixel of the scene. Considering a monostatic configuration, the scattering matrix S is defined as follows:   (1) where , are copolar terms,  is the cross polar term. This can be represented with the target scattering vector  using the Pauli basis as: (2) where  is the transpose operator. In Sentinel-1configuration (VV/VH), where there is no knowledge of  , scattering vector  can be written as: (3) In order to generate an interferogram, each target vector  can be projected onto a unitary complex vector  . Result of this step obtains the scattering coefficient μ defined as: (4) where i corresponds to two images, and * represents the conjugate operator. The scattering coefficient μ is a new channel or polarization state which is a linear combination of the Pauli vector elements. In this regard, all interferometry techniques can be extended from single-pol configuration to a desired polarization state by using (4) and (5). The projection vector for dual-pol data defined as:   (5) where  and  are two real parameters whose ranges are finite and known and are related to the geometrical and electromagnetic properties of the targets. The parameter  represents the type of scattering mechanism and  corresponds to orientation of scattering. In our research, the main purpose of polarimetric optimization is to search in a two-dimensional space,  and , to find an optimum projection vector, .   2.3 Amplitude Dispersion Index Optimization   In order to generate a polarimetric form of , it is sufficient to replace scattering coefficient, , in (6) by  as define in (4):       (6)       (7)   The main issue in the ADI optimization is finding a projection vector  for each pixel, which leads to minimize the  value. Results & Discussion Our results confirm that the algorithm substantially improves the PSInSAR performance, increasing the number of PS pixels with respect to standard PSI, and increasing the phase quality of selected pixels. The results reveal that using the optimum scattering mechanism increases the number of PSC about 2.6 times and PS density about 2 times than using single channel datasets. Also, the effectiveness of the method is evaluated in urban and non-urban regions. The experimental results showed that the method was successful to increase the final set of PS pixels in both regions significantly. Conclusion In summary, it can be inferred that the polarimetric optimization method is successful to increase the number of the final set of PS pixels in different regions, significantly.

Today''s digital elevation models have many applications in various fields including engineering projects and management of natural resources and etc. One of these applications is a topographic correction in InSAR to find the amount and rate of displacements. The purpose of this paper is to compare the effects of two digital elevation models with a resolution of 30 m and 90 m in order to obtain displacement rates from radar images. Persist Scatterer and Small Baselines methods were used to compute the displacement rate in the region. Processing was performed with both models SRTM and ASTER. The maximum difference between the results from two elevation models is observed in areas with a high elevation difference. In both methods، the number of persist scatterers in the case of model ASTER is less than model SRTM. In areas with low elevation differences، the results of two elevation models are very similar to each other. But in areas with high topography، the low resolution elevation model does not have the ability to deliver results with appropriate accuracy. In PS method there are 0. 2 mm difference in maximum and 1. 1 mm in minimum of displacement rate field and in Small Baselines method، these rates were 4 and 1 mm respectively. In order to better evaluate the results، six points in the region were examined. The maximum difference between the results was 4 mm. This difference is significant at the ten percent level of confidence. As a result، in areas of high topography، it is necessary to use the more accurate digital elevation model to achieve higher accuracy.

SAR Image matching is a critical step in the radargrammetry, interferometry, DSM Generation and change detection applications of SAR image data. Image matching procedure in these images is much more difficult than the optical images due to high speckle noise and geometric distortions defining by layover, shadow, and foreshortening. Therefore, study on an efficient SAR image matching could be useful. In this paper, a multi-step strategy for this issue is proposed. These steps include SAR image despeckling, point feature extraction, feature allocation, local image matching and global image matching. In the proposed multi-step method we used current algorithms in photogrammetry and computer vision. In each step, several algorithms are experimented and compared to specify the final algorithm for that step. In our experiments, we used a pair of TerraSAR-X single-look slant-range complex (SSC) images with short and long baselines that were acquired over the city of Jam, southern Iran, in spotlight mode. The result shows that the proposed method could match appropriate number of points in both images with high accuracy and reliability. For example it could match 211 points with 1.9 pixel accuracy for long base line image pair with size of 700×700 pixels and 1603 points with 1.22 pixel accuracy for short base line image pair with size of 400×400 pixels. Therefore, the proposed SAR multi-step image matching strategy is appropriate for coarse matching level.