Next Article in Journal
E-Region Field-Aligned Irregularities in the Middle of a Solar Eclipse Observed by a Bistatic Radar
Previous Article in Journal
Revisiting Ice Flux and Mass Balance of the Lambert Glacier–Amery Ice Shelf System Using Multi-Remote-Sensing Datasets, East Antarctica
Previous Article in Special Issue
FLATSIM: The [email protected] LArge-Scale Multi-Temporal Sentinel-1 InterferoMetry Service
 
 
Article

Compressed SAR Interferometry in the Big Data Era

1
UMR TETIS, INRAE, University of Montpellier, 34090 Montpellier, France
2
Independent Researcher, 34090 Montpellier, France
*
Author to whom correspondence should be addressed.
Academic Editor: Gwanggil Jeon
Remote Sens. 2022, 14(2), 390; https://doi.org/10.3390/rs14020390
Received: 16 December 2021 / Revised: 6 January 2022 / Accepted: 10 January 2022 / Published: 14 January 2022
(This article belongs to the Special Issue Radar Interferometry in Big Data Era)
Modern Synthetic Aperture Radar (SAR) missions provide an unprecedented massive interferometric SAR (InSAR) time series. The processing of the Big InSAR Data is challenging for long-term monitoring. Indeed, as most deformation phenomena develop slowly, a strategy of a processing scheme can be worked on reduced volume data sets. This paper introduces a novel ComSAR algorithm based on a compression technique for reducing computational efforts while maintaining the performance robustly. The algorithm divides the massive data into many mini-stacks and then compresses them. The compressed estimator is close to the theoretical Cramer–Rao lower bound under a realistic C-band Sentinel-1 decorrelation scenario. Both persistent and distributed scatterers (PSDS) are exploited in the ComSAR algorithm. The ComSAR performance is validated via simulation and application to Sentinel-1 data to map land subsidence of the salt mine Vauvert area, France. The proposed ComSAR yields consistently better performance when compared with the state-of-the-art PSDS technique. We make our PSDS and ComSAR algorithms as an open-source TomoSAR package. To make it more practical, we exploit other open-source projects so that people can apply our PSDS and ComSAR methods for an end-to-end processing chain. To our knowledge, TomoSAR is the first public domain tool available to jointly handle PS and DS targets. View Full-Text
Keywords: InSAR; PSI; PSDS; ComSAR; Vauvert; subsidence; TomoSAR InSAR; PSI; PSDS; ComSAR; Vauvert; subsidence; TomoSAR
Show Figures

Figure 1

MDPI and ACS Style

Ho Tong Minh, D.; Ngo, Y.-N. Compressed SAR Interferometry in the Big Data Era. Remote Sens. 2022, 14, 390. https://doi.org/10.3390/rs14020390

AMA Style

Ho Tong Minh D, Ngo Y-N. Compressed SAR Interferometry in the Big Data Era. Remote Sensing. 2022; 14(2):390. https://doi.org/10.3390/rs14020390

Chicago/Turabian Style

Ho Tong Minh, Dinh, and Yen-Nhi Ngo. 2022. "Compressed SAR Interferometry in the Big Data Era" Remote Sensing 14, no. 2: 390. https://doi.org/10.3390/rs14020390

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop