Synthetic aperture radar (SAR) represents one of the most popular and advanced remote sensing techniques for Earth monitoring, creating 2D images with a high spatial resolution. It is also notable that it is a system that does not depend on weather conditions or daylight to conduct the data acquisition. The main objective of the paper is to estimate the elevation profile of the scenes, using methods of tomographic processing of synthetic aperture radar images, on Sentinel-1 data. The city of Prague (Czech Republic), along with the surrounding areas was selected as the test region. The urban region will guarantee the high coherence of the acquisitions. To achieve the main objective, a multi-temporal data set will be built, consisting of 20 SAR acquisitions of the Sentinel-1 satellite, suitable for tomographic processing. Each of the 20 images has the same polarization and orbit orientation. For the image pre-processing stage, the SNAP interferometric processor was used. The preprocessing steps consist of: co-registration, generation of the flattened interferograms and their filtering. The final results of these steps were exported to Matlab for further processing. The reflectivity profile of each pixel will be estimated using two non-parametric filters: Beam-Forming and Capon, and the elevation maps of the area will be generated.
Advanced Synthetic Aperture Radar (SAR) processing techniques have gradually become a powerful tool for earth observation. The ubiquitous problem of noise filtering represents also a challenge in the SAR signal processing field. In case of the analysis of land regions, the identification of a general algorithm, suitable for the large-scale processing, represents an additional challenge, due to the diversity of the areas’ characteristics. More specifically, two main classes of scattering mechanisms can be defined: Persistent Scatterers (PS), coherent, point-wise targets, and Distributed Scatterers (DS), mechanisms with moderate coherence, whose contribution spreads across multiple pixels of the images. Due to their different characteristics, the joint processing of PS and DS was difficult to address, until the development of the SqueeSAR algorithm. This method, based on the joint processing of statistical homogeneous pixels, was successfully implemented in the field of SAR Interferometry, which exploits solely the phase of the focused acquisitions. The objective of this work is to evaluate the applicability of the SqueeSAR principle in the frame of SAR Tomography, a multitemporal SAR processing technique which also has the ability to separate interfering scattering mechanisms.
Interferometric phase filtering is an essential step of the processing chains related to Synthetic Aperture Radar interferometry (InSAR), which are usually used for the generation of digital elevation models and Earth’s surface displacement maps through differential InSAR techniques. Performance of the involved processing steps, and in particular of the phase unwrapping operations, are greatly influenced by the ability of the employed filtering algorithms to mitigate the artefacts that affect the interferometric phase. An important impediment with a direct impact on the unwrapping process is due to the presence of spatial inconsistencies in the interferometric phase, called residues. More specifically, these phase inconsistencies are made evident by calculating the curl of the phase differences over any spatial closed loop, which is different from zero in the case a residue is present in an interferogram. In this paper, a comparative study of four different phase filtering algorithms are addressed: coherent averaging, modified median filtering, Lee filter and Goldstein-Wiener filter. The algorithms’ performances are mainly assessed by their capacity to reduce the number of the above-mentioned phase residues in the noise-filtered interferograms.
Persistent Scatterers Interferometry (PS-InSAR) has become a popular method in remote sensing because of its capability to measure terrain deformations with very high accuracy. It relies on multiple Synthetic Aperture Radar (SAR) acquisitions, to monitor points with stable proprieties over time, called Persistent Scatterers (PS)[1]. These points are unaffected by temporal decorrelation, therefore by analyzing their interferometric phase variation we can estimate the scene’s deformation rates within a given time interval. In this work, we apply two incoherent detection algorithms to identify Persistent Scatterers candidates in the city of Focșani, Romania. The first method studies the variation of targets’ intensities along the SAR acquisitions and the second method analyzes the spectral proprieties of the scatterers. The algorithms were implemented on a dataset containing 11 complex images of the region covering Buzău, Brăila and Focșani cities. Images were acquired by Sentinel-1 satellite in a time span of 5 months, from October 2014 to February 2015. The processing chain follows the requirements imposed by the new C-band SAR images delivered by the Sentinel-1 satellite (launched in April 2014) imaging in Interferometric Wide (IW) mode. Considering the particularities of the TOPS (Terrain Observation with Progressive Scans in Azimuth) imaging mode[2], special requirements had to be considered for pre-processing steps. The PS detection algorithms were implemented in Gamma RS program, a software which contains various function packages dedicated to SAR images focalization, analysis and processing.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.