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Remote Sensing
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Radar Interferometry in Big Data Era
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Radar Interferometry in Big Data Era

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".

Deadline for manuscript submissions: closed (15 December 2021) | Viewed by 31849

Special Issue Editor

Dr. Habil. Dinh Ho Tong Minh

E-Mail Website
Guest Editor
IRSTEA-UMR TETIS
Interests: InSAR signal processing; tomography; subsidence; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Synthetic Aperture Radar (SAR) Interferometry (InSAR) is a unique technology that widely used to measure ground subsidence and has already shown its ability to map such phenomena on a large spatial scale with millimetric accuracy from space. Sentinel-1 and the near future NISAR missions offer an unprecedented multi-temporal dataset of InSAR. Consequently, the processing of the Big Data is challenging for InSAR analysis techniques. This Special Issue is intended to present high-quality scientific review papers of existing achievements in the development and applications of InSAR techniques, or research papers that describe improved methods of InSAR in Big Data era, including multi-temporal analysis, phase calibration, phase unwrapping, geocoded single look complex InSAR;  improved methods of interpretation of InSAR data for forest biomass and forest characteristics, and urban analysis; as well as demonstration InSAR Big Data applications (i.,e., nation scale, delta-wide, or area greater than 10000 km2) for elevation determinations, earthquake, volcano, land subsidence and relative deformed topics. The recent Deep Learning technique for InSAR applications will also be included in this Special Issue.

Dr. Habil. Dinh Ho Tong Minh
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • SAR interferometry
  • Big Data
  • Subsidence
  • Sentinel-1
  • DEM
  • Phase unwrapping
  • Tomography
  • Deep Learning

Published Papers (6 papers)

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Research

13 pages, 5792 KiB  
Article
Compressed SAR Interferometry in the Big Data Era
by Dinh Ho Tong Minh and Yen-Nhi Ngo
Remote Sens. 2022, 14(2), 390; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14020390 - 14 Jan 2022
Cited by 6 | Viewed by 5798
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Radar Interferometry in Big Data Era)
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29 pages, 26881 KiB  
Article
FLATSIM: The ForM@Ter LArge-Scale Multi-Temporal Sentinel-1 InterferoMetry Service
by Franck Thollard, Dominique Clesse, Marie-Pierre Doin, Joëlle Donadieu, Philippe Durand, Raphaël Grandin, Cécile Lasserre, Christophe Laurent, Emilie Deschamps-Ostanciaux, Erwan Pathier, Elisabeth Pointal, Catherine Proy and Bernard Specht
Remote Sens. 2021, 13(18), 3734; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183734 - 17 Sep 2021
Cited by 11 | Viewed by 3734
Abstract
The purpose of the ForM@Ter LArge-scale multi-Temporal Sentinel-1 InterferoMetry service (FLATSIM) is the massive processing of Sentinel-1 data using multi-temporal interferometric synthetic aperture radar (InSAR) over large areas, i.e., greater than 250,000 km2. It provides the French ForM@ter scientific community with [...] Read more.
The purpose of the ForM@Ter LArge-scale multi-Temporal Sentinel-1 InterferoMetry service (FLATSIM) is the massive processing of Sentinel-1 data using multi-temporal interferometric synthetic aperture radar (InSAR) over large areas, i.e., greater than 250,000 km2. It provides the French ForM@ter scientific community with automatically processed products using a state of the art processing chain based on a small baseline subset approach, namely the New Small Baseline (NSBAS). The service results from a collaboration between the scientific team that develops and maintains the NSBAS processing chain and the French Spatial Agency (CNES) that mirrors the Sentinel-1 data. The proximity to Sentinel-1 data, the NSBAS workflow, and the specific optimizations to make NSBAS processing massively parallel for the CNES high performance computing infrastructure ensures the efficiency of the chain, especially in terms of input and output, which is the key for the success of such a service. The FLATSIM service is made of a production module, a delivery module and a user access module. Products include interferograms, surface line of sight velocity, phase delay time series and auxiliary data. Numerous quality indicators are provided for an in-depth analysis of the quality and limits of the results. The first national call in 2020 for region of interest ended up with 8 regions spread over the world with scientific interests, including seismology, tectonics, volcano-tectonics, and hydrological cycle. To illustrate the FLATSIM capabilities, an analysis is shown here on two processed regions, the Afar region in Ethiopa, and the eastern border of the Tibetan Plateau. Full article
(This article belongs to the Special Issue Radar Interferometry in Big Data Era)
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18 pages, 12944 KiB  
Article
InSAR Coherence Analysis for Wetlands in Alberta, Canada Using Time-Series Sentinel-1 Data
by Meisam Amani, Valentin Poncos, Brian Brisco, Fatemeh Foroughnia, Evan R. DeLancey and Sadegh Ranjbar
Remote Sens. 2021, 13(16), 3315; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163315 - 21 Aug 2021
Cited by 13 | Viewed by 3964
Abstract
Wetlands are valuable natural resources which provide numerous services to the environment. Many studies have demonstrated the potential of various types of remote sensing datasets and techniques for wetland mapping and change analysis. However, there are a relatively low number of studies that [...] Read more.
Wetlands are valuable natural resources which provide numerous services to the environment. Many studies have demonstrated the potential of various types of remote sensing datasets and techniques for wetland mapping and change analysis. However, there are a relatively low number of studies that have investigated the application of the Interferometric Synthetic Aperture Radar (InSAR) coherence products for wetland studies, especially over large areas. Therefore, in this study, coherence products over the entire province of Alberta, Canada (~661,000 km2) were generated using the Sentinel-1 data acquired from 2017 to 2020. Then, these products along with large amount of wetland reference samples were employed to assess the separability of different wetland types and their trends over time. Overall, our analyses showed that coherence can be considered as an added value feature for wetland classification and monitoring. The Treed Bog and Shallow Open Water classes showed the highest and lowest coherence values, respectively. The Treed Wetland and Open Wetland classes were easily distinguishable. When analyzing the wetland subclasses, it was observed that the Treed Bog and Shallow Open Water classes can be easily discriminated from other subclasses. However, there were overlaps between the signatures of the other wetland subclasses, although there were still some dates where these classes were also distinguishable. The analysis of multi-temporal coherence products also showed that the coherence products generated in spring/fall (e.g., May and October) and summer (e.g., July) seasons had the highest and lowest coherence values, respectively. It was also observed that wetland classes preserved coherence during the leaf-off season (15 August–15 October) while they had relatively lower coherence during the leaf-on season (i.e., 15 May–15 August). Finally, several suggestions for future studies were provided. Full article
(This article belongs to the Special Issue Radar Interferometry in Big Data Era)
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15 pages, 2863 KiB  
Article
Offline-Online Change Detection for Sentinel-1 InSAR Time Series
by Ekbal Hussain, Alessandro Novellino, Colm Jordan and Luke Bateson
Remote Sens. 2021, 13(9), 1656; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13091656 - 23 Apr 2021
Cited by 8 | Viewed by 3481
Abstract
Traditional applications of Interferometric Synthetic Aperture Radar (InSAR) data involved inverting an interferogram stack to determine the average displacement velocity. While this approach has useful applications in continuously deforming regions, much information is lost by simply fitting a line through the time series. [...] Read more.
Traditional applications of Interferometric Synthetic Aperture Radar (InSAR) data involved inverting an interferogram stack to determine the average displacement velocity. While this approach has useful applications in continuously deforming regions, much information is lost by simply fitting a line through the time series. Thanks to regular acquisitions across most of the the world by the ESA Sentinel-1 satellite constellation, we are now in a position to explore opportunities for near-real time deformation monitoring. In this paper we present a statistical approach for detecting offsets and gradient changes in InSAR time series. Our key assumption is that 5 years of Sentinel-1 data is sufficient to calculate the population standard deviation of the detection variables. Our offset detector identifies statistically significant peaks in the first, second and third difference series. The gradient change detector identifies statistically significant movements in the second derivative series. We exploit the high spatial resolution of Sentinel-1 data and the spatial continuity of geophysical deformation signals to filter out false positive detections that arise due to signal noise. When combined with near-real time processing of InSAR data these detectors, particularly the gradient change, could be used to detect incipient ground deformation associated with geophysical phenomena, for example from landslides or volcanic eruptions. Full article
(This article belongs to the Special Issue Radar Interferometry in Big Data Era)
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29 pages, 12710 KiB  
Article
Vegetated Target Decorrelation in SAR and Interferometry: Models, Simulation, and Performance Evaluation
by Andrea Monti-Guarnieri, Marco Manzoni, Davide Giudici, Andrea Recchia and Stefano Tebaldini
Remote Sens. 2020, 12(16), 2545; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12162545 - 07 Aug 2020
Cited by 21 | Viewed by 4619
Abstract
The paper addresses the temporal stability of distributed targets, particularly referring to vegetation, to evaluate the degradation affecting synthetic aperture radar (SAR) imaging and repeat-pass interferometry, and provide efficient SAR simulation schemes for generating big dataset from wide areas. The models that are [...] Read more.
The paper addresses the temporal stability of distributed targets, particularly referring to vegetation, to evaluate the degradation affecting synthetic aperture radar (SAR) imaging and repeat-pass interferometry, and provide efficient SAR simulation schemes for generating big dataset from wide areas. The models that are mostly adopted in literature are critically reviewed, and aim to study decorrelation in a range of time (from hours to days), of interest for long-term SAR, such as ground-based or geosynchronous, or repeat-pass SAR interferometry. It is shown that none of them explicitly account for a decorrelation occurring in the short-term. An explanation is provided, and a novel temporal decorrelation model is proposed to account for that fast decorrelation. A formal method is developed to evaluate the performance of SAR focusing, and interferometry on a homogenous, stationary scene, in terms of Signal-to-Clutter Ratio (SCR), and interferometric coherence. Finally, an efficient implementation of an SAR simulator capable of handling the realistic case of heterogeneous decorrelation over a wide area is discussed. Examples are given by assuming two geostationary SAR missions in C and X band. Full article
(This article belongs to the Special Issue Radar Interferometry in Big Data Era)
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18 pages, 1300 KiB  
Article
Radar Interferometry: 20 Years of Development in Time Series Techniques and Future Perspectives
by Dinh HO TONG MINH, Ramon Hanssen and Fabio Rocca
Remote Sens. 2020, 12(9), 1364; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12091364 - 26 Apr 2020
Cited by 76 | Viewed by 8110
Abstract
The research and improvement of methods to be used for deformation measurements from space is a challenge. From the previous 20 years, time series Synthetic Aperture Radar (SAR) interferometry techniques have proved for their ability to provide millimeter-scale deformation measurements over time. This [...] Read more.
The research and improvement of methods to be used for deformation measurements from space is a challenge. From the previous 20 years, time series Synthetic Aperture Radar (SAR) interferometry techniques have proved for their ability to provide millimeter-scale deformation measurements over time. This paper aims to provide a review of such techniques developed in the last twenty years. We first recall the background of interferometric SAR (InSAR). We then provide an overview of the InSAR time series methods developed in the literature, describing their principles and advancements. Finally, we highlight challenges and future perspectives of the InSAR in the Big Data era. Full article
(This article belongs to the Special Issue Radar Interferometry in Big Data Era)
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