Hybrid Technique of the Branch-Cut and the Quality-Guided for Insar Phase Unwrapping

2021 ◽  
pp. 2150040
Author(s):  
Tarek Bentahar ◽  
Atef Bentahar ◽  
Riad Saidi ◽  
Hichem Mayache ◽  
Karim Ferroudji
Keyword(s):  
Processing Time ◽  
Path Following ◽  
Radar Imaging ◽  
Phase Unwrapping ◽  
Synthetic Aperture ◽  
Hybrid Technique ◽  
Interferometric Synthetic Aperture Radar ◽  
Surface Change ◽  
Phase Images ◽  
Branch Cut

Phase unwrapping is a key step for interferometric synthetic aperture radar imaging. It is widely used for earth mapping and surface change detection. Several residue-immune phase unwrapping algorithms have been proposed; among them, we find branch-cut and quality-guided in the path-following category. Branch-cut methods are usually faster than the quality-guided techniques; however, the accuracy of their unwrapped phase images is lower. In this paper, a hybrid model which combines both algorithms is proposed in order to establish a satisfactory compromise between processing time and accuracy. In order to verify the usefulness of the proposed hybridization, it is tested on simulated and real inSAR data. The obtained results are compared with the two methods under several relevant metrics.

scholarly journals PSInSAR Study of Lyngenfjord Norway, using TerraSAR-X Data

2018 ◽  
Vol IV-5 ◽  
pp. 245-251
Author(s):  
U. Asopa ◽  
S. Kumar ◽  
P. K. Thakur
Keyword(s):  
Phase Shift ◽  
Temporal Analysis ◽  
Phase Unwrapping ◽  
Synthetic Aperture ◽  
Interferometric Synthetic Aperture Radar ◽  
Point Distribution ◽  
Subpixel Accuracy ◽  
Persistent Scatterer ◽  
Temporal And Spatial ◽  
Temporal Basis

<p><strong>Abstract.</strong> In this research paper, focus is given on exploring the potential of Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR) technique for the measurement of landslide, which is the extension of existing DInSAR technique. In PSInSAR technique, the movement is measured by finding the phase shift in the scatterers present in the study area through the course of time. The backscattering of such a scatterer does not change during the study. By using this technique, 32 datasets acquired over a period of time i.e. from 2009 to 2011 over the area of Troms County of Lyngen Fjord, Norway are analysed. The dataset utilised are acquired with TerraSAR-X and TanDEM-X pair, in Stripmap mode of acquisition. Coregistration of dataset with subpixel accuracy is done with master images is done to align all the dataset correctly. APS estimation is done in order to remove the phase decorrelation caused by the atmosphere, movement, etc. using algorithms for phase unwrapping which allowed the processing of sparse data and the effect of atmosphere is reduced by doing analysis on temporal basis of the phase shift in interferograms of successive datasets. By this study it has been tried to show the estimation of shift can be done by the temporal analysis of the data acquired by TerraSAR-X. The velocity output is displayed in a map reflecting the velocity of movement. Apart from this, the data properties such as baseline distribution both temporal and spatial are displayed in a chart. Other outputs obtained are the atmospheric Phase Screen, sparse point distribution, reflectivity map of the study area etc. are displayed using a map of terrain. The output velocity obtained of the terrain movement is found to be in the range of &amp;minus;40<span class="thinspace"></span>mm/yr to &amp;minus;70<span class="thinspace"></span>mm/yr.</p>

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

&lt;p&gt;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.&lt;/p&gt;

scholarly journals ANALYSIS OF PERFORMANCE OF FLAT EARTH PHASE REMOVAL METHODS

2018 ◽  
Vol XLII-5 ◽  
pp. 207-209
Author(s):  
K. Desai ◽  
P. Joshi ◽  
S. Chirakkal ◽  
D. Putrevu ◽  
R. Ghosh
Keyword(s):  
Remote Sensing ◽  
Synthetic Aperture Radar ◽  
Surface Deformation ◽  
Paper Analysis ◽  
Phase Unwrapping ◽  
Evaluation Index ◽  
Synthetic Aperture ◽  
Interferometric Synthetic Aperture Radar ◽  
Imaging Geometry ◽  
Analysis Of Performance

<p><strong>Abstract.</strong> Interferometric synthetic aperture radar (InSAR) has been widely used in remote sensing field, which can reflect actual topographic trend or possible surface deformation. Due to the orbit attitude influence, the flat-earth phase usually causes the interferogram dense and difficult to be used in further procedures. Before phase unwrapping, interferogram must be flattened to derive accurate topographic or deformation information. In this paper, analysis of performance of two methods of flat-earth removal is done. First method uses imaging geometry and second method uses precise orbital information. Further, 3-degree, 5-degree and 7-degree polynomials are fitted in the method using precise orbital information. Validation is done both visually and empirically using entropy as the evaluation index.</p>

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