scholarly journals Accurate Antenna Pattern Modeling for Phased Array Antennas in SAR Applications—Demonstration on TerraSAR-X

2009 ◽  
Vol 2009 ◽  
pp. 1-9 ◽  
Author(s):  
Markus Bachmann ◽  
Marco Schwerdt ◽  
Benjamin Bräutigam
Keyword(s):  
Synthetic Aperture ◽  
Active Antenna ◽  
Sar Images ◽  
Phased Array Antennas ◽  
Antenna Steering ◽  
Pattern Modeling ◽  
High Flexibility ◽  
Sar Processing ◽  
Antenna Model ◽  
Better Than

The high flexibility and tight accuracy requirements of today's spaceborne synthetic aperture radar (SAR) systems require innovative technologies to calibrate and process the SAR images. To perform accurate pattern correction during SAR processing, an Antenna Model is used to derive the multitude of different antenna beams generated by active antenna steering. The application of such an Antenna Model could be successfully demonstrated for the TerraSAR-X mission, launched in 2007. The methodology and the results of the inorbit verification with an achieved accuracy of better than  dB is reviewed in this paper in detail showing its outstanding accuracy.

scholarly journals SodSAR: A Tower-Based 1–10 GHz SAR System for Snow, Soil and Vegetation Studies

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6702
Author(s):  
Jorge Jorge Ruiz ◽  
Risto Vehmas ◽  
Juha Lemmetyinen ◽  
Josu Uusitalo ◽  
Janne Lahtinen ◽  
...  
Keyword(s):  
Synthetic Aperture ◽  
The Arctic ◽  
Target Range ◽  
Network Analyzer ◽  
Sar Images ◽  
Finnish Meteorological Institute ◽  
Radar Calibration ◽  
Loss Of Coherence ◽  
Better Than ◽  
Meteorological Institute

We introduce SodSAR, a fully polarimetric tower-based wide frequency (1–10 GHz) range Synthetic Aperture Radar (SAR) aimed at snow, soil and vegetation studies. The instrument is located in the Arctic Space Centre of the Finnish Meteorological Institute in Sodankylä, Finland. The system is based on a Vector Network Analyzer (VNA)-operated scatterometer mounted on a rail allowing the formation of SAR images, including interferometric pairs separated by a temporal baseline. We present the description of the radar, the applied SAR focusing technique, the radar calibration and measurement stability analysis. Measured stability of the backscattering intensity over a three-month period was observed to be better than 0.5 dB, when measuring a target with a known radar cross section. Deviations of the estimated target range were in the order of a few cm over the same period, indicating also good stability of the measured phase. Interforometric SAR (InSAR) capabilities are also discussed, and as a example, the coherence of subsequent SAR acquisitions over the observed boreal forest stand are analyzed over increasing temporal baselines. The analysis shows good conservation of coherence in particular at L-band, while higher frequencies are susceptible to loss of coherence in particular for dense vegetation. The potential of the instrument for satellite calibration and validation activities is also discussed.

On the Performance of the MPI Algorithm for the Retrieval of Significant Wave Height From ERS Synthetic Aperture Radar

2003 ◽  
Author(s):  
Nelson Violante-Carvalho ◽  
Ian S. Robinson
Keyword(s):  
Synthetic Aperture Radar ◽  
Wave Height ◽  
Significant Wave Height ◽  
Synthetic Aperture ◽  
Wave Spectra ◽  
Sar Images ◽  
Significant Wave ◽  
Directional Wave ◽  
Better Than ◽  
Aperture Radar

Spaceborne Synthetic Aperture Radar (SAR) is to date the only source of two dimensional directional wave spectra with continuous and global coverage when operated in the so-called SAR Wave Mode (SWM). Since the launch in 1991 of the first European Remote Sensing Satellite ERS-1 and more recently with ENVISAT millions of SWM imagettes containing detailed spectral information are now available in quasi-real time. This huge amount of directional wave data has opened up many exciting possibilities for the improvement of our knowledge of the dynamics of ocean waves. However the retrieval of wave spectra from SAR images is not a trivial exercise due to the nonlinearities involved in the mapping mechanism. The Max-Planck Institut (MPI) scheme was the first ever proposed and most widely used algorithm to retrieve directional wave spectra from SAR images. In this work significant wave height retrieved from SAR images using the MPI scheme are compared against one year of directional buoy measurements obtained in deep water and against WAM spectra. Our results show that for periods shorter than 12 seconds the WAM model performs better than the MPI method, even considering the fact that the model is used as first guess to the MPI scheme. However, for periods longer than 12 seconds (the part of the spectrum directly observed by SAR) the MPI method performs better than WAM. This is in contrast with the results obtained by Voorrips et al. (2001), who found that the performance of the WAM model is superior even when only the low wavenumber part of the spectrum is considered.

scholarly journals Use of the SAR Shadowing Effect for Deforestation Detection with Sentinel-1 Time Series

2018 ◽  
Vol 10 (8) ◽  
pp. 1250 ◽  
Author(s):  
Alexandre Bouvet ◽  
Stéphane Mermoz ◽  
Marie Ballère ◽  
Thierry Koleck ◽  
Thuy Le Toan
Keyword(s):  
Time Series ◽  
Detection Rate ◽  
Large Scale ◽  
Detection Method ◽  
Earth Observation ◽  
Synthetic Aperture ◽  
Sar Images ◽  
Shadowing Effect ◽  
Potential Exploitation ◽  
Better Than

To detect deforestation using Earth Observation (EO) data, widely used methods are based on the detection of temporal changes in the EO measurements within the deforested patches. In this paper, we introduce a new indicator of deforestation obtained from synthetic aperture radar (SAR) images, which relies on a geometric artifact that appears when deforestation happens, in the form of a shadow at the border of the deforested patch. The conditions for the appearance of these shadows are analyzed, as well as the methods that can be employed to exploit them to detect deforestation. The approach involves two steps: (1) detection of new shadows; (2) reconstruction of the deforested patch around the shadows. The launch of Sentinel-1 in 2014 has opened up opportunities for a potential exploitation of this approach in large-scale applications. A deforestation detection method based on this approach was tested in a 600,000 ha site in Peru. A detection rate of more than 95% is obtained for samples larger than 0.4 ha, and the method was found to perform better than the optical-based UMD-GLAD Forest Alert dataset both in terms of spatial and temporal detection. Further work needed to exploit this approach at operational levels is discussed.

scholarly journals Analysis Method of Errors (Motion and Atmospheric) in Synthetic Aperture Radar (SAR) Images

2005 ◽  
Vol 3 ◽  
pp. 395-398 ◽  
Author(s):  
A. Danklmayer ◽  
K. A. Camara de Macedo ◽  
R. Scheiber ◽  
T. Boerner ◽  
M. Chandra
Keyword(s):  
Synthetic Aperture ◽  
Linear Phase ◽  
Analysis Method ◽  
Sar Images ◽  
Data Set ◽  
Phase Errors ◽  
Processing Procedure ◽  
System Data ◽  
Airborne Sar ◽  
Sar Processing

Abstract. A method to allow the analysis of the effects of motion and atmospheric errors in SAR images is here presented. The objective of the method is to allow the visualization of the effects of motion errors and atmospheric artefacts on the processed (focused) SAR image. The method is intended to allow the analysis of the interaction of motion and atmospheric errors with the adopted SAR processing procedure and motion compensation algorithms. In this article the analysis method has been applied and tested to a C-Band E-SAR (DLR airborne SAR system) data set where we see that the effects of linear and non-linear phase errors observed are in agreement with the theory.

scholarly journals Realizing Target Detection in SAR Images Based on Multiscale Superpixel Fusion

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1643
Author(s):  
Ming Liu ◽  
Shichao Chen ◽  
Fugang Lu ◽  
Mengdao Xing ◽  
Jingbiao Wei
Keyword(s):  
Synthetic Aperture Radar ◽  
False Alarm ◽  
False Alarm Rate ◽  
Target Detection ◽  
Synthetic Aperture ◽  
False Alarms ◽  
Sar Image ◽  
Constant False Alarm Rate ◽  
Sar Images ◽  
Complex Scenes

For target detection in complex scenes of synthetic aperture radar (SAR) images, the false alarms in the land areas are hard to eliminate, especially for the ones near the coastline. Focusing on the problem, an algorithm based on the fusion of multiscale superpixel segmentations is proposed in this paper. Firstly, the SAR images are partitioned by using different scales of superpixel segmentation. For the superpixels in each scale, the land-sea segmentation is achieved by judging their statistical properties. Then, the land-sea segmentation results obtained in each scale are combined with the result of the constant false alarm rate (CFAR) detector to eliminate the false alarms located on the land areas of the SAR image. In the end, to enhance the robustness of the proposed algorithm, the detection results obtained in different scales are fused together to realize the final target detection. Experimental results on real SAR images have verified the effectiveness of the proposed algorithm.

scholarly journals Analysis of Moving Object Imaging from Compressively Sensed SAR Data in the Presence of Dictionary Mismatch

2013 ◽  
Vol 2013 ◽  
pp. 1-16 ◽  
Author(s):  
Ahmed Shaharyar Khwaja ◽  
Muhammad Naeem ◽  
Alagan Anpalagan
Keyword(s):  
Iterative Scheme ◽  
Reconstruction Error ◽  
Synthetic Aperture ◽  
Moving Target ◽  
Target Imaging ◽  
Cause Of Error ◽  
Moving Target Imaging ◽  
Velocity Mismatch ◽  
Object Imaging ◽  
Better Than

We present compressed sensing (CS) synthetic aperture radar (SAR) moving target imaging in the presence of dictionary mismatch. Unlike existing work on CS SAR moving target imaging, we analyze the sensitivity of the imaging process to the mismatch and present an iterative scheme to cope with dictionary mismatch. We analyze and investigate the effects of mismatch in range and azimuth positions, as well as range velocity. The analysis reveals that the reconstruction error increases with the mismatch and range velocity mismatch is the major cause of error. Instead of using traditional Laplacian prior (LP), we use Gaussian-Bernoulli prior (GBP) for CS SAR imaging mismatch. The results show that the performance of GBP is much better than LP. We also provide the Cramer-Rao Bounds (CRB) that demonstrate theoretically the lowering of mean square error between actual and reconstructed result by using the GBP. We show that a combination of an upsampled dictionary and the GBP for reconstruction can deal with position mismatch effectively. We further present an iterative scheme to deal with the range velocity mismatch. Numerical and simulation examples demonstrate the accuracy of the analysis as well as the effectiveness of the proposed upsampling and iterative scheme.

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