scholarly journals Biomass Growth from Multi-Temporal TanDEM-X Interferometric Synthetic Aperture Radar Observations of a Boreal Forest Site

2018 ◽  
Vol 10 (4) ◽  
pp. 603 ◽  
Author(s):  
Jan Askne ◽  
Henrik Persson ◽  
Lars Ulander
Keyword(s):  
Boreal Forest ◽  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Forest Site ◽  
Interferometric Synthetic Aperture Radar ◽  
Biomass Growth ◽  
Radar Observations ◽  
Multi Temporal ◽  
Aperture Radar

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 Multi-Temporal Small Baseline Interferometric SAR Algorithms: Error Budget and Theoretical Performance

2021 ◽  
Vol 13 (4) ◽  
pp. 557
Author(s):  
Antonio Pepe
Keyword(s):  
Synthetic Aperture Radar ◽  
Phase Unwrapping ◽  
Synthetic Aperture ◽  
Interferometric Synthetic Aperture Radar ◽  
Error Budget ◽  
Multi Temporal ◽  
Small Baseline ◽  
Time Inconsistent ◽  
Interferometric Sar ◽  
Aperture Radar

Multi-temporal interferometric synthetic aperture radar (MT-InSAR) techniques are well recognized as useful tools for detecting and monitoring Earth’s surface temporal changes. In this work, the fundamentals of error noise propagation and perturbation theories are applied to derive the ground displacement products’ theoretical error bounds of the small baseline (SB) differential interferometric synthetic aperture radar algorithms. A general formulation of the least-squares (LS) optimization problem, representing the SB methods implementation’s core, was adopted in this research study. A particular emphasis was placed on the effects of time-uncorrelated phase unwrapping mistakes and time-inconsistent phase disturbances in sets of SB interferograms, leading to artefacts in the attainable InSAR products. Moreover, this study created the theoretical basis for further developments aimed at quantifying the error budget of the time-uncorrelated phase unwrapping mistakes and studying time-inconsistent phase artefacts for the generation of InSAR data products. Some experiments, performed by considering a sequence of synthetic aperture radar (SAR) images collected by the ASAR sensor onboard the ENVISAT satellite, supported the developed theoretical framework.

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