scholarly journals Joint Correction of Tropospheric and Orbital Errors in SAR Differential Interferograms

2021 ◽  
Vol 44 ◽  
Author(s):  
Kamel Hasni ◽  
Bachir Gourine ◽  
Houaria Namaoui ◽  
Mohammed El Amin Larabi ◽  
Saddam Housseyn Allal
Keyword(s):  
Synthetic Aperture Radar ◽  
Satellite Imagery ◽  
Noise Source ◽  
Synthetic Aperture ◽  
Tropospheric Delay ◽  
Coseismic Displacement ◽  
Displacement Error ◽  
Interferometric Phase ◽  
Signal Path ◽  
Differential Interferogram

Synthetic Aperture Radar (SAR) satellite imagery is a source of data widely employed in the quantification and analysis of an earthquake coseismic displacement. However, due to the signal path along the atmosphere and to other sources, the interferometric phase becomes compromised. In this work, a methodology for the correction of tropospheric and orbital errors in the differential interferogram is presented. This methodology was applied to a couple of Sentinel-1A data. The phenomenon studied was the 11th November 2018 Zeribet el Oued earthquake, Mw. 5.2 (The state of Biskra, South East of Algeria). It was possible to correct both tropospheric and orbital errors, where the dominant one was the tropospheric delay, a displacement error of 4 cm was added to the differential interferogram by this noise source. The correction of orbital error led to a better interpretation of the coseismic displacement. 

scholarly journals Development of a Radar Reflector Kit for Older Adults to Use to Signal Their Location and Needs in a Large-Scale Earthquake Disaster

2021 ◽  
Vol 13 (10) ◽  
pp. 1883
Author(s):  
Yuma Morisaki ◽  
Makoto Fujiu ◽  
Ryoichi Furuta ◽  
Junichi Takayama
Keyword(s):  
Older Adults ◽  
Synthetic Aperture Radar ◽  
Satellite Imagery ◽  
Disaster Response ◽  
Large Scale ◽  
Synthetic Aperture ◽  
Earthquake Disaster ◽  
Large Numbers ◽  
The World ◽  
Vulnerable People

In Japan, older adults account for the highest proportion of the population of any country in the world. When large-scale earthquake disasters strike, large numbers of casualties are known to particularly occur among seniors. Many are physically or mentally vulnerable and require assistance during the different phases of disaster response, including rescue, evacuation, and living in an evacuation center. However, the growing number of older adults has made it difficult, after a disaster, to quickly gather information on their locations and assess their needs. The authors are developing a proposal to enable vulnerable people to signal their location and needs in the aftermath of a disaster to response teams by deploying radar reflectors that can be detected in synthetic aperture radar (SAR) satellite imagery. The purpose of this study was to develop a radar reflector kit that seniors could easily assemble in order to make this proposal feasible in practice. Three versions of the reflector were tested for detectability, and a sample of older adults was asked to assemble the kits and provide feedback regarding problems they encountered and regarding their interest in using the reflectors in the event of a large-scale disaster.

A deep learning approach for efficient multi-temporal interferometric synthetic aperture radar (MT-InSAR) processing

2021 ◽  
Author(s):  
Ashutosh Tiwari ◽  
Avadh BIhari Narayan ◽  
Onkar Dikshit
Keyword(s):  
Deep Learning ◽  
Synthetic Aperture Radar ◽  
Phase Unwrapping ◽  
Synthetic Aperture ◽  
Interferometric Synthetic Aperture Radar ◽  
Displacement Estimation ◽  
Interferometric Phase ◽  
Selection Step ◽  
Multi Temporal ◽  
Aperture Radar

<p>Multi-temporal interferometric synthetic aperture radar (MT-InSAR) technique has been effectively used to monitor deformation events over the last two decades. The processing steps generally involve pixel selection, phase unwrapping and displacement estimation. The pixel selection step takes most of the processing time, while a reliable method for phase unwrapping is still not available. This study demonstrates the effect of using deep learning (DL) architectures for MT-InSAR processing. The architectures are applied to reduce time computations and further to improve the quality of pixel selection. Some promising results for pixel selection have been shown earlier with the proposed architecture. In this study, we investigate the performance of the proposed architectures on newer datasets with larger temporal interval. To achieve this objective, the models are retrained with interferometric stacks covering larger temporal period and large time steps (for better estimation of interferometric phase components). Pixel selection results are compared with those obtained using open access algorithms used for MT-InSAR processing.</p>

scholarly journals CLASSIFIER FUSION OF HIGH-RESOLUTION OPTICAL AND SYNTHETIC APERTURE RADAR (SAR) SATELLITE IMAGERY FOR CLASSIFICATION IN URBAN AREA

2014 ◽  
Vol XL-2/W3 ◽  
pp. 25-29 ◽  
Author(s):  
T. Alipour Fard ◽  
M. Hasanlou ◽  
H. Arefi
Keyword(s):  
High Resolution ◽  
Synthetic Aperture Radar ◽  
Satellite Imagery ◽  
Optical Sensors ◽  
Classification Accuracy ◽  
Classifier Fusion ◽  
Synthetic Aperture ◽  
Sensor Technology ◽  
The Difference ◽  
Aperture Radar

This study concerned with fusion of synthetic aperture radar and optical satellite imagery. Due to the difference in the underlying sensor technology, data from synthetic aperture radar (SAR) and optical sensors refer to different properties of the observed scene and it is believed that when they are fused together, they complement each other to improve the performance of a particular application. In this paper, two category of features are generate and six classifier fusion operators implemented and evaluated. <br><br> Implementation results show significant improvement in the classification accuracy.

scholarly journals Mitigating Atmospheric Effects in InSAR Stacking Based on Ensemble Forecasting with a Numerical Weather Prediction Model

2021 ◽  
Vol 13 (22) ◽  
pp. 4670
Author(s):  
Fangjia Dou ◽  
Xiaolei Lv ◽  
Huiming Chai
Keyword(s):  
Synthetic Aperture Radar ◽  
Numerical Weather Prediction ◽  
Deformation Rate ◽  
Weather Prediction ◽  
Ensemble Forecasting ◽  
Synthetic Aperture ◽  
Tropospheric Delay ◽  
Numerical Weather ◽  
Atmospheric Delay ◽  
Aperture Radar

The interferometric synthetic aperture radar (InSAR) technique is widely utilized to measure ground-surface displacement. One of the main limitations of the measurements is the atmospheric phase delay effects. For satellites with shorter wavelengths, the atmospheric delay mainly consists of the tropospheric delay influenced by temperature, pressure, and water vapor. Tropospheric delay can be calculated using numerical weather prediction (NWP) model at the same moment as synthetic aperture radar (SAR) acquisition. Scientific researchers mainly use ensemble forecasting to produce better forecasts and analyze the uncertainties caused by physic parameterizations. In this study, we simulated the relevant meteorological parameters using the ensemble scheme of the stochastic physic perturbation tendency (SPPT) based on the weather research forecasting (WRF) model, which is one of the most broadly used NWP models. We selected an area in Foshan, Guangdong Province, in the southeast of China, and calculated the corresponding atmospheric delay. InSAR images were computed through data from the Sentinel-1A satellite and mitigated by the ensemble mean of the WRF-SPPT results. The WRF-SPPT method improves the mitigating effect more than WRF simulation without ensemble forecasting. The atmospherically corrected InSAR phases were used in the stacking process to estimate the linear deformation rate in the experimental area. The root mean square errors (RMSE) of the deformation rate without correction, with WRF-only correction, and with WRF-SPPT correction were calculated, indicating that ensemble forecasting can significantly reduce the atmospheric delay in stacking. In addition, the ensemble forecasting based on a combination of initial uncertainties and stochastic physic perturbation tendencies showed better correction performance compared with the ensemble forecasting generated by a set of perturbed initial conditions without considering the model&#x2019;s uncertainties.

scholarly journals InSAR Modeling of Geophysics Measurements

2020 ◽  
Author(s):  
Andon Lazarov ◽  
Dimitar Minchev ◽  
Chavdar Minchev
Keyword(s):  
Synthetic Aperture Radar ◽  
Numerical Experiments ◽  
Synthetic Aperture ◽  
Surface Model ◽  
Interferometric Synthetic Aperture Radar ◽  
Signal Model ◽  
Reconstruction Procedure ◽  
Basic Parameters ◽  
Differential Interferogram ◽  
Aperture Radar

In the present work, the geometry and basic parameters of interferometric synthetic aperture radar (InSAR) geophysics system are addressed. Equations of pixel height and displacement evaluation are derived. Synthetic aperture radar (SAR) signal model based on linear frequency modulation (LFM) waveform and image reconstruction procedure are suggested. The concept of pseudo InSAR measurements, interferogram, and differential interferogram generation is considered. Interferogram and differential interferogram are generated based on a surface model and InSAR measurements. Results of numerical experiments are provided.

scholarly journals Applied Research on InSAR and GPS Data Fusion in Deformation Monitoring

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Ziwen Zhang ◽  
Xuelian Wang ◽  
Yongdong Wu ◽  
Zengpeng Zhao ◽  
Yang E
Keyword(s):  
Data Fusion ◽  
Synthetic Aperture Radar ◽  
Land Subsidence ◽  
Inverse Distance Weighting ◽  
Synthetic Aperture ◽  
Tropospheric Delay ◽  
Gps Data ◽  
Distance Weighting ◽  
Inverse Distance ◽  
Aperture Radar

With the enrichment of land subsidence monitoring means, data fusion of multisource land subsidence data has gradually become a research hotspot. The Interferometry Synthetic Aperture Radar (InSAR) is a potential Earth observation approach, and it has been verified to have a variety of applications in measuring ground movement, urban subsidence, and landslides but similar to Global Positioning System (GPS). The InSAR observation accuracy and measurements are affected by the tropospheric delay error as well as by the Earth’s ionospheric and tropospheric layers. In order to rectify the InSAR result, there is a need to interpolate the GPS-derived tropospheric delay. Keeping in view of the above, this research study has presented an improved Inverse Distance Weighting (IIDW) interpolation method based on Inverse Distance Weighting (IDW) interpolation by using Sentinel-1 radar satellite image provided by European Space Agency (ESA) and the measured data from the Continuously Operating Reference Stations (CORS) provided by the Survey and Mapping Office of the Lands Department of Hong Kong. Furthermore, the corrected differential tropospheric delay correction is used to correct the InSAR image. The experimental results show that the correction of tropospheric delay by IIDW interpolation not only improves the accuracy of Differential Interferometry Synthetic Aperture Radar (D-InSAR) but also provides a new idea for the solution of InSAR and GPS data fusion.

scholarly journals Evaluation of Approaches for Mapping Tidal Wetlands of the Chesapeake and Delaware Bays

2019 ◽  
Vol 11 (20) ◽  
pp. 2366
Author(s):  
Brian T. Lamb ◽  
Maria A. Tzortziou ◽  
Kyle C. McDonald
Keyword(s):  
Random Forest ◽  
Synthetic Aperture Radar ◽  
Satellite Imagery ◽  
Regional Scale ◽  
Google Earth ◽  
Synthetic Aperture ◽  
Tidal Wetlands ◽  
Study Sites ◽  
Aperture Radar ◽  
Emergent Wetlands

The spatial extent and vegetation characteristics of tidal wetlands and their change are among the biggest unknowns and largest sources of uncertainty in modeling ecosystem processes and services at the land-ocean interface. Using a combination of moderate-high spatial resolution (≤30 meters) optical and synthetic aperture radar (SAR) satellite imagery, we evaluated several approaches for mapping and characterization of wetlands of the Chesapeake and Delaware Bays. Sentinel-1A, Phased Array type L-band Synthetic Aperture Radar (PALSAR), PALSAR-2, Sentinel-2A, and Landsat 8 imagery were used to map wetlands, with an emphasis on mapping tidal marshes, inundation extents, and functional vegetation classes (persistent vs. non-persistent). We performed initial characterizations at three target wetlands study sites with distinct geomorphologies, hydrologic characteristics, and vegetation communities. We used findings from these target wetlands study sites to inform the selection of timeseries satellite imagery for a regional scale random forest-based classification of wetlands in the Chesapeake and Delaware Bays. Acquisition of satellite imagery, raster manipulations, and timeseries analyses were performed using Google Earth Engine. Random forest classifications were performed using the R programming language. In our regional scale classification, estuarine emergent wetlands were mapped with a producer’s accuracy greater than 88% and a user’s accuracy greater than 83%. Within target wetland sites, functional classes of vegetation were mapped with over 90% user’s and producer’s accuracy for all classes, and greater than 95% accuracy overall. The use of multitemporal SAR and multitemporal optical imagery discussed here provides a straightforward yet powerful approach for accurately mapping tidal freshwater wetlands through identification of non-persistent vegetation, as well as for mapping estuarine emergent wetlands, with direct applications to the improved management of coastal wetlands.

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