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Multi-temporal Synthetic Aperture Radar
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Multi-temporal Synthetic Aperture Radar

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

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 25992

Special Issue Editors

Dr. Donato Amitrano

E-Mail Website
Guest Editor
Surrey Space Centre, University of Surrey, Guildford GU2 7XH, UK
Interests: remote sensing; SAR; Earth observation; electromagnetic scattering, computer vision; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Raffaella Guida

E-Mail Website
Guest Editor
Surrey Space Centre, Department of Electrical and Electronic Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU1 3LY, UK
Interests: remote sensing; synthetic aperture radar; maritime surveillance; disaster monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. Pasquale Iervolino

E-Mail Website
Guest Editor
Surrey Space Centre, University of Surrey, Guildford GU2 7XH, UK
Interests: maritime surveillance; data fusion; ship detection; electromagnetic modeling; microwave remote sensing

Special Issue Information

Dear Colleagues,

Satellite technologies have widely demonstrated their effectiveness in continuous environmental monitoring and territorial planning and management. Today, the remote sensing community is experiencing an unprecedented abundance of data which is boosting the development of more and more applications for temporal analysis of our rapidly changing planet. This is true especially concerning SAR data, which, under the aegis of the Copernicus Programme of the European Space Agency, are distributed for the first time free of charge to any kind of user.

However, this huge availability of data is posing new problems to the SAR scientific community. Visualization, change-detection, clustering, and labelling of long time-series in a big-data scenario is still an open problem, as well as their exploitation in combination with other sensory data in a multi-frequency environment.

The objective of this Special Issue is to delineate the state-of the-art in SAR time-series data processing methodologies and to facilitate the exploitation of this technology in the industrial sector through the development of new applications in the downstream sector.

Contributions are expected on (but not limited to) the following topics:

  • Multi-temporal SAR data analytics
  • Visualization of time-series data
  • Multi-sensor and multi-frequency data fusion
  • Integration of SAR data with other remote sensing products
  • SAR data exploitation and information retrieval for land, ocean, urban areas and forestry applications

Dr. Donato Amitrano
Dr. Raffaella Guida
Dr. Pasquale Iervolino
Guest Editors

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

  • Earth observation
  • synthetic aperture radar
  • time-series
  • data analytics
  • multi-temporal
  • data fusion
  • data assimilation
  • land monitoring
  • ocean monitoring
  • urban area monitoring
  • forestry

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Published Papers (5 papers)

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Research

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16 pages, 12111 KiB  
Article
Land Subsidence in Wuhan Revealed Using a Non-Linear PSInSAR Approach with Long Time Series of COSMO-SkyMed SAR Data
by Haonan Jiang, Timo Balz, Francesca Cigna and Deodato Tapete
Remote Sens. 2021, 13(7), 1256; https://doi.org/10.3390/rs13071256 - 25 Mar 2021
Cited by 46 | Viewed by 6200
Abstract
Wuhan is an important city in central China, with a rapid development that has led to increasingly serious land subsidence over the last decades. Most of the existing Interferometric Synthetic Aperture Radar (InSAR) subsidence monitoring studies in Wuhan are either short-term investigations—and thus [...] Read more.
Wuhan is an important city in central China, with a rapid development that has led to increasingly serious land subsidence over the last decades. Most of the existing Interferometric Synthetic Aperture Radar (InSAR) subsidence monitoring studies in Wuhan are either short-term investigations—and thus can only detect this process within limited time periods—or combinations of different Synthetic Aperture Radar (SAR) datasets with temporal gaps in between. To overcome these constraints, we exploited nearly 300 high-resolution COSMO-SkyMed StripMap HIMAGE scenes acquired between 2012 and 2019 to monitor the long-term subsidence process affecting Wuhan and to reveal its spatiotemporal variations. The results from the Persistent Scatterer Interferometric SAR (PSInSAR) processing highlight several clearly observable subsidence zones. Three of them (i.e., Houhu, Xinrong, and Guanggu) are affected by serious subsidence rates and non-linear temporal behavior, and are investigated in this paper in more detail. The subsidence in Houhu is caused by soft soil consolidation and compression. Soil mechanics are therefore used to estimate when the subsidence is expected to finish and to calculate the degree of consolidation for each year. The COSMO-SkyMed PSInSAR results indicate that the area has entered the late stage of consolidation and compression and is gradually stabilizing. The subsidence curve found for the area around Xinrong shows that the construction of an underground tract of the subway Line 21 caused large-scale settlement in this area. The temporal granularity of the PSInSAR time series also allows precise detection of a rebound phase following a major flooding event in 2016. In the southern industrial park of Guanggu, newly detected subsidence was found. The combination of the subsidence curve with an optical time-series image analysis indicates that urban construction is the main trigger of deformation in this area. While this study unveils previously unknown characters of land subsidence in Wuhan and clarifies the relationship with the urban causative factors, it also proves the benefits of non-linear PSInSAR in the analysis of the temporal evolution of such processes in dynamic and expanding cities. Full article
(This article belongs to the Special Issue Multi-temporal Synthetic Aperture Radar)
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17 pages, 30452 KiB  
Article
Monitoring Land Surface Deformation Associated with Gold Artisanal Mining in the Zaruma City (Ecuador)
by Lorenzo Ammirati, Nicola Mondillo, Ricardo Adolfo Rodas, Chester Sellers and Diego Di Martire
Remote Sens. 2020, 12(13), 2135; https://doi.org/10.3390/rs12132135 - 3 Jul 2020
Cited by 28 | Viewed by 4403
Abstract
Underground mining can produce subsidence phenomena, especially if orebodies are surficial or occur in soft rocks. In some countries, illegal mining is a big problem for environmental, social and economic reasons. However, when unauthorized excavation is conducted underground, it is even more dangerous [...] Read more.
Underground mining can produce subsidence phenomena, especially if orebodies are surficial or occur in soft rocks. In some countries, illegal mining is a big problem for environmental, social and economic reasons. However, when unauthorized excavation is conducted underground, it is even more dangerous because it can produce unexpected surficial collapses in areas not adequately monitored. For this reason, it is important to find quick and economic techniques able to give information about the spatial and temporal development of uncontrolled underground activities in order to improve the risk management. In this work, the differential interferometric synthetic aperture radar (DInSAR) technique, implemented in the SUBSOFT software, has been used to study terrain deformation related to illegal artisanal mining in Ecuador. The study area is located in Zaruma (southeast of El Oro province), a remarkable site for Ecuadorian cultural heritage where, at the beginning of the 2017, a local school collapsed, due to sinkhole phenomena that occurred around the historical center. The school, named “Inmaculada Fe y Alegria”, was located in an area where mining activity was forbidden. For this study, the surface deformations that occurred in the Zaruma area from 2015 to 2019 were detected by using the Sentinel-1 data derived from the Europe Space Agency of the Copernicus Program. Deformations of the order of five centimeters were revealed both in correspondence of known exploitation tunnels, but also in areas where the presence of tunnels had not been verified. In conclusion, this study allowed to detect land surface movements related to underground mining activity, confirming that the DInSAR technique can be applied for monitoring mining-related subsidence. Full article
(This article belongs to the Special Issue Multi-temporal Synthetic Aperture Radar)
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19 pages, 2730 KiB  
Article
A Novel Active Contours Model for Environmental Change Detection from Multitemporal Synthetic Aperture Radar Images
by Salman Ahmadi and Saeid Homayouni
Remote Sens. 2020, 12(11), 1746; https://doi.org/10.3390/rs12111746 - 29 May 2020
Cited by 12 | Viewed by 4044
Abstract
In this paper, we propose a novel approach based on the active contours model for change detection from synthetic aperture radar (SAR) images. In order to increase the accuracy of the proposed approach, a new operator was introduced to generate a difference image [...] Read more.
In this paper, we propose a novel approach based on the active contours model for change detection from synthetic aperture radar (SAR) images. In order to increase the accuracy of the proposed approach, a new operator was introduced to generate a difference image from the before and after change images. Then, a new model of active contours was developed for accurately detecting changed regions from the difference image. The proposed model extracts the changed areas as a target feature from the difference image based on training data from changed and unchanged regions. In this research, we used the Otsu histogram thresholding method to produce the training data automatically. In addition, the training data were updated in the process of minimizing the energy function of the model. To evaluate the accuracy of the model, we applied the proposed method to three benchmark SAR data sets. The proposed model obtains 84.65%, 87.07%, and 96.26% of the Kappa coefficient for Yellow River Estuary, Bern, and Ottawa sample data sets, respectively. These results demonstrated the effectiveness of the proposed approach compared to other methods. Another advantage of the proposed model is its high speed in comparison to the conventional methods. Full article
(This article belongs to the Special Issue Multi-temporal Synthetic Aperture Radar)
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21 pages, 6659 KiB  
Article
Change Analysis in Urban Areas Based on Statistical Features and Temporal Clustering Using TerraSAR-X Time-Series Images
by Jili Yuan, Xiaolei Lv, Fangjia Dou and Jingchuan Yao
Remote Sens. 2019, 11(8), 926; https://doi.org/10.3390/rs11080926 - 16 Apr 2019
Cited by 77 | Viewed by 4150
Abstract
The existing unsupervised multitemporal change detection approaches for synthetic aperture radar (SAR) images based on the pixel level usually suffer from the serious influence of speckle noise, and the classification accuracy of temporal change patterns is liable to be affected by the generation [...] Read more.
The existing unsupervised multitemporal change detection approaches for synthetic aperture radar (SAR) images based on the pixel level usually suffer from the serious influence of speckle noise, and the classification accuracy of temporal change patterns is liable to be affected by the generation method of similarity matrices and the pre-specified cluster number. To address these issues, a novel time-series change detection method with high efficiency is proposed in this paper. Firstly, spatial feature extraction using local statistical information on patches is conducted to reduce the noise and for subsequent temporal grouping. Secondly, a density-based clustering method is adopted to categorize the pixel series in the temporal dimension, in view of its efficiency and robustness. Change detection and classification results are then obtained by a fast differential strategy in the final step. The experimental results and analysis of synthetic and realistic time-series SAR images acquired by TerraSAR-X in urban areas demonstrate the effectiveness of the proposed method, which outperforms other approaches in terms of both qualitative results and quantitative indices of macro F1-scores and micro F1-scores. Furthermore, we make the case that more temporal change information for buildings can be obtained, which includes when the first and last detected change occurred and the frequency of changes. Full article
(This article belongs to the Special Issue Multi-temporal Synthetic Aperture Radar)
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Review

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45 pages, 17677 KiB  
Review
Earth Environmental Monitoring Using Multi-Temporal Synthetic Aperture Radar: A Critical Review of Selected Applications
by Donato Amitrano, Gerardo Di Martino, Raffaella Guida, Pasquale Iervolino, Antonio Iodice, Maria Nicolina Papa, Daniele Riccio and Giuseppe Ruello
Remote Sens. 2021, 13(4), 604; https://doi.org/10.3390/rs13040604 - 8 Feb 2021
Cited by 26 | Viewed by 6000
Abstract
Microwave remote sensing has widely demonstrated its potential in the continuous monitoring of our rapidly changing planet. This review provides an overview of state-of-the-art methodologies for multi-temporal synthetic aperture radar change detection and its applications to biosphere and hydrosphere monitoring, with special focus [...] Read more.
Microwave remote sensing has widely demonstrated its potential in the continuous monitoring of our rapidly changing planet. This review provides an overview of state-of-the-art methodologies for multi-temporal synthetic aperture radar change detection and its applications to biosphere and hydrosphere monitoring, with special focus on topics like forestry, water resources management in semi-arid environments and floods. The analyzed literature is categorized on the base of the approach adopted and the data exploited and discussed in light of the downstream remote sensing market. The purpose is to highlight the main issues and limitations preventing the diffusion of synthetic aperture radar data in both industrial and multidisciplinary research contexts and the possible solutions for boosting their usage among end-users. Full article
(This article belongs to the Special Issue Multi-temporal Synthetic Aperture Radar)
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