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Remote Sensing
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Monitoring Urban Areas with Satellite SAR Remote Sensing
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Monitoring Urban Areas with Satellite SAR Remote Sensing

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

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 15776

Special Issue Editors

Dr. Oriol Monserrat

E-Mail Website
Guest Editor
Remote Sensing Department, Division of Geomatics, Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), Av. Gauss, 7 E-08860 Castelldefels (Barcelona), Spain
Interests: remote sensing data processing; SAR data; SAR interferometry; geohazard monitoring; landslide mapping; building monitoring; land subsidence
Special Issues, Collections and Topics in MDPI journals
Dr. Qi Gao

E-Mail Website
Guest Editor
Remote Sensing Department, Division of Geomatics, Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), Av. Gauss, 7 E-08860 Castelldefels, Barcelona, Spain
Interests: SAR and SAR data processing; SAR interferometry; land deformation; hydrology; water management; hazard monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. Anna Barra

E-Mail Website
Guest Editor
Remote Sensing Department, Division of Geomatics, Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), Av. Gauss, 7, E-08860 Castelldefels, Barcelona, Spain
Interests: remote sensing; SAR data; SAR interferometry; geohazard monitoring; landslide mapping and monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. Jorge P. Galve

E-Mail Website
Guest Editor
Department of Geodynamics, University of Granada, Campus de Fuente Nueva S/N, 18071 Granada, Spain
Interests: natural hazards; geomorphology; GIS; SAR interferometry
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, SAR sensors have gained relevance as a tool to monitor urban environments. The wide range of available sensors mounted on different platforms such as satellite, drone or ground-based, together with the strong effort made in research have increased the range of applications in such types of environments. At present, the available satellite-based SAR sensors and imagery provide a rich set of possibilities. The huge archive of images covering almost 30 years of data acquisition allow performing historical analysis. This type of analysis is currently widely used as a tool for assessing the potential hazards affecting an area before starting works, understanding existing damages, mapping changes, and creating products which are useful for urban planning and development. Moreover, the availability of constellations like Sentinel-1 A and B that guarantees worldwide coverage each 6–12 days constitutes a clear step forward, consolidating the use of SAR-based techniques as tools for continuously assessing the potential impact of ongoing deformations, to evaluate the effectiveness of mitigation actions or to monitor critical changes in the urban environment. Finally, the wide range of available resolutions ranging from several meters up to some centimeters enables working at different levels, such as monitoring a whole urban area or just a single building. This Special Issue is focused on new approaches to urban monitoring with satellite SAR data. The Special Issue aims to collect the latest innovative research results related to this topic. These can include new data processing algorithms and procedures, results based on new types of SAR data, and the development of innovative urban monitoring applications. The topics of interest include but are not limited to:

  • New interferometric developments for urban monitoring: deformations, changes, damage mapping;
  • Different types of data exploitation, polarimetric SAR data, new constellations such as Ice-Eye of Capella space, etc.;
  • Multisensor multiscale approaches, data integration, integration with optical data;
  • Development of innovative applications for urban planning and monitoring;
  • Validation exercises;
  • Review papers on the use of satellite SAR data or urban deformation monitoring.

Dr. Oriol Monserrat
Dr. Qi Gao
Ms. Anna Barra
Dr. Jorge P. Galve
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

  • SAR
  • DInSAR
  • Classification
  • Displacements
  • Monitoring
  • Change detection

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Published Papers (3 papers)

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Research

18 pages, 9876 KiB  
Article
Fast Urban Land Cover Mapping Exploiting Sentinel-1 and Sentinel-2 Data
by Naomi Petrushevsky, Marco Manzoni and Andrea Monti-Guarnieri
Remote Sens. 2022, 14(1), 36; https://doi.org/10.3390/rs14010036 - 22 Dec 2021
Cited by 14 | Viewed by 3424
Abstract
The rapid change and expansion of human settlements raise the need for precise remote-sensing monitoring tools. While some Land Cover (LC) maps are publicly available, the knowledge of the up-to-date urban extent for a specific instance in time is often missing. The lack [...] Read more.
The rapid change and expansion of human settlements raise the need for precise remote-sensing monitoring tools. While some Land Cover (LC) maps are publicly available, the knowledge of the up-to-date urban extent for a specific instance in time is often missing. The lack of a relevant urban mask, especially in developing countries, increases the burden on Earth Observation (EO) data users or requires them to rely on time-consuming manual classification. This paper explores fast and effective exploitation of Sentinel-1 (S1) and Sentinel-2 (S2) data for the generation of urban LC, which can be frequently updated. The method is based on an Object-Based Image Analysis (OBIA), where one Multi-Spectral (MS) image is used to define clusters of similar pixels through super-pixel segmentation. A short stack (<2 months) of Synthetic Aperture Radar (SAR) data is then employed to classify the clusters, exploiting the unique characteristics of the radio backscatter from human-made targets. The repeated illumination and acquisition geometry allows defining robust features based on amplitude, coherence, and polarimetry. Data from ascending and descending orbits are combined to overcome distortions and decrease sensitivity to the orientation of structures. Finally, an unsupervised Machine Learning (ML) model is used to separate the signature of urban targets in a mixed environment. The method was validated in two sites in Portugal, with diverse types of LC and complex topography. Comparative analysis was performed with two state-of-the-art high-resolution solutions, which require long sensing periods, indicating significant agreement between the methods (averaged accuracy of around 90%). Full article
(This article belongs to the Special Issue Monitoring Urban Areas with Satellite SAR Remote Sensing)
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22 pages, 16395 KiB  
Article
TomoSAR 3D Reconstruction for Buildings Using Very Few Tracks of Observation: A Conditional Generative Adversarial Network Approach
by Shihong Wang, Jiayi Guo, Yueting Zhang, Yuxin Hu, Chibiao Ding and Yirong Wu
Remote Sens. 2021, 13(24), 5055; https://doi.org/10.3390/rs13245055 - 13 Dec 2021
Cited by 8 | Viewed by 3882
Abstract
SAR tomography (TomoSAR) is an important technology for three-dimensional (3D) reconstruction of buildings through multiple coherent SAR images. In order to obtain sufficient signal-to-noise ratio (SNR), typical TomoSAR applications often require dozens of scenes of SAR images. However, limited by time and cost, [...] Read more.
SAR tomography (TomoSAR) is an important technology for three-dimensional (3D) reconstruction of buildings through multiple coherent SAR images. In order to obtain sufficient signal-to-noise ratio (SNR), typical TomoSAR applications often require dozens of scenes of SAR images. However, limited by time and cost, the available SAR images are often only 3–5 scenes in practice, which makes the traditional TomoSAR technique unable to produce satisfactory SNR and elevation resolution. To tackle this problem, the conditional generative adversarial network (CGAN) is proposed to improve the TomoSAR 3D reconstruction by learning the prior information of building. Moreover, the number of tracks required can be reduced to three. Firstly, a TomoSAR 3D super-resolution dataset is constructed using high-quality data from the airborne array and low-quality data obtained from a small amount of tracks sampled from all observations. Then, the CGAN model is trained to estimate the corresponding high-quality result from the low-quality input. Airborne data experiments prove that the reconstruction results are improved in areas with and without overlap, both qualitatively and quantitatively. Furthermore, the network pretrained on the airborne dataset is directly used to process the spaceborne dataset without any tuning, and generates satisfactory results, proving the effectiveness and robustness of our method. The comparative experiment with nonlocal algorithm also shows that the proposed method has better height estimation and higher time efficiency. Full article
(This article belongs to the Special Issue Monitoring Urban Areas with Satellite SAR Remote Sensing)
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31 pages, 8160 KiB  
Article
Neural Network-Based Urban Change Monitoring with Deep-Temporal Multispectral and SAR Remote Sensing Data
by Georg Zitzlsberger, Michal Podhorányi, Václav Svatoň, Milan Lazecký and Jan Martinovič
Remote Sens. 2021, 13(15), 3000; https://doi.org/10.3390/rs13153000 - 30 Jul 2021
Cited by 10 | Viewed by 7160
Abstract
Remote-sensing-driven urban change detection has been studied in many ways for decades for a wide field of applications, such as understanding socio-economic impacts, identifying new settlements, or analyzing trends of urban sprawl. Such kinds of analyses are usually carried out manually by selecting [...] Read more.
Remote-sensing-driven urban change detection has been studied in many ways for decades for a wide field of applications, such as understanding socio-economic impacts, identifying new settlements, or analyzing trends of urban sprawl. Such kinds of analyses are usually carried out manually by selecting high-quality samples that binds them to small-scale scenarios, either temporarily limited or with low spatial or temporal resolution. We propose a fully automated method that uses a large amount of available remote sensing observations for a selected period without the need to manually select samples. This enables continuous urban monitoring in a fully automated process. Furthermore, we combine multispectral optical and synthetic aperture radar (SAR) data from two eras as two mission pairs with synthetic labeling to train a neural network for detecting urban changes and activities. As pairs, we consider European Remote Sensing (ERS-1/2) and Landsat 5 Thematic Mapper (TM) for 1991–2011 and Sentinel 1 and 2 for 2017–2021. For every era, we use three different urban sites—Limassol, Rotterdam, and Liège—with at least 500km2 each, and deep observation time series with hundreds and up to over a thousand of samples. These sites were selected to represent different challenges in training a common neural network due to atmospheric effects, different geographies, and observation coverage. We train one model for each of the two eras using synthetic but noisy labels, which are created automatically by combining state-of-the-art methods, without the availability of existing ground truth data. To combine the benefit of both remote sensing types, the network models are ensembles of optical- and SAR-specialized sub-networks. We study the sensitivity of urban and impervious changes and the contribution of optical and SAR data to the overall solution. Our implementation and trained models are available publicly to enable others to utilize fully automated continuous urban monitoring. Full article
(This article belongs to the Special Issue Monitoring Urban Areas with Satellite SAR Remote Sensing)
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