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Remote Sensing
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Artificial Intelligence for SAR Applications in Environmental Monitoring
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Artificial Intelligence for SAR Applications in Environmental Monitoring

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

Deadline for manuscript submissions: closed (1 June 2022) | Viewed by 7464

Special Issue Editors

Dr. Oscar Mora

E-Mail Website
Guest Editor
Cartographic and Geological Institute of Catalonia (ICGC), Barcelona, Spain
Interests: Synthetic Aperture Radar (SAR); SAR Interferometry; Persistent Scatterer Interferometry (PSI); Artificial Intelligence (AI); Deep Learning (DL)
Prof. Jordi Vitrià

E-Mail Website
Guest Editor
University of Barcelona, Barcelona, Spain
Interests: machine learning; deep learning; data science; computer vision applications

Special Issue Information

Dear Colleagues,

Although the first SAR (Synthetic Aperture Radar) sensors were put into orbit more than two decades ago, it has been in recent years that various missions have begun to provide time series of images with a high revisit frequency. Among these missions we can find satellites in X band (such as TerraSAR-X or Cosmo-SkyMed), in C band (such as Sentinel-1A/B or Radarsat-2), or in L band (SAOCOM-1A/B). The information on the amplitude and phase of the SAR images has been used for different purposes, which include the detection of landcover changes, monitoring of water bodies, detection of oil spills, wind measurements or ground motion time series, among others.

On the other hand, the exploitation of large amounts of data, such as the time series of images, has taken a great qualitative leap with the new techniques of Artificial Intelligence (AI) and Deep Learning (DL). Although initially focused on the analysis of conventional images, great advances are currently being made in studies with Earth Observation data.

In this Special Issue we intend to compile a series of papers that merge the use of SAR images with AI techniques for environmental monitoring. SAR data should be the main focus of the study, but other data sources can also be used in the papers presented. Contributions that present new methodologies in the spatial and/or temporal analysis of SAR images are welcome. 

Dr. Oscar Mora
Prof. Jordi Vitrià
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
  • radar interferometry
  • artificial intelligence
  • deep learning
  • monitoring
  • time series
  • environment

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Published Papers (2 papers)

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Research

23 pages, 10821 KiB  
Article
On the Joint Exploitation of Satellite DInSAR Measurements and DBSCAN-Based Techniques for Preliminary Identification and Ranking of Critical Constructions in a Built Environment
by Annalisa Mele, Autilia Vitiello, Manuela Bonano, Andrea Miano, Riccardo Lanari, Giovanni Acampora and Andrea Prota
Remote Sens. 2022, 14(8), 1872; https://doi.org/10.3390/rs14081872 - 13 Apr 2022
Cited by 34 | Viewed by 2567
Abstract
The need for widespread structural safety checks represents a stimulus for the research of advanced techniques for structural monitoring at the scale of single constructions or wide areas. In this work, a strategy to preliminarily identify and rank possible critical constructions in a [...] Read more.
The need for widespread structural safety checks represents a stimulus for the research of advanced techniques for structural monitoring at the scale of single constructions or wide areas. In this work, a strategy to preliminarily identify and rank possible critical constructions in a built environment is presented, based on the joint exploitation of satellite radar remote sensing measurements and artificial intelligence (AI) techniques. The satellite measurements are represented by the displacement time series obtained through the Differential Synthetic Aperture Radar Interferometry (DInSAR) technique known as full resolution Small BAseline Subset (SBAS) approach, while the exploited AI technique is represented by the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) methodology. The DBSCAN technique is applied to the SBAS-DInSAR products relevant to the achieved Persistent Scatterers (PSs), to identify clusters of pixels corresponding to buildings within the investigated area. The analysis of the deformation evolution of each building cluster is performed in terms of velocity rates and statistics on the DInSAR measurements. Synthetic deformation maps of the areas are then retrieved to identify critical buildings. The proposed methodology is applied to three areas within the city of Rome (Italy), imaged by the COSMO-SkyMed SAR satellite constellation from ascending and descending orbits (in the time interval 2011–2019). Starting from the DInSAR measurements, the DBSCAN algorithm provides the automatic clustering of buildings within the three selected areas. Exploiting the derived deformation maps of each study area, a preliminary identification and ranking of critical buildings is achieved, thus confirming the validity of the proposed approach. Full article
(This article belongs to the Special Issue Artificial Intelligence for SAR Applications in Environmental Monitoring)
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Graphical abstract

18 pages, 2072 KiB  
Article
Fusion of Multi-Temporal PAZ and Sentinel-1 Data for Crop Classification
by Mario Busquier, Rubén Valcarce-Diñeiro, Juan M. Lopez-Sanchez, Javier Plaza, Nilda Sánchez and Benjamín Arias-Pérez
Remote Sens. 2021, 13(19), 3915; https://doi.org/10.3390/rs13193915 - 30 Sep 2021
Cited by 6 | Viewed by 3003
Abstract
The accurate identification of crops is essential to help environmental sustainability and support agricultural policies. This study presents the use of a Spanish radar mission, PAZ, to classify agricultural areas with a very high spatial resolution. PAZ was recently launched, and it operates [...] Read more.
The accurate identification of crops is essential to help environmental sustainability and support agricultural policies. This study presents the use of a Spanish radar mission, PAZ, to classify agricultural areas with a very high spatial resolution. PAZ was recently launched, and it operates at X band, joining the synthetic aperture radar (SAR) constellation along with TerraSAR-X and TanDEM-X satellites. Owing to its novelty and its ability to classify crop areas (both taking individually its time series and blending with the Sentinel-1 series), it has been tested in an agricultural area of the central-western part of Spain during 2020. The random forest algorithm was selected to classify the time series under five alternatives of standalone/fused data. The map accuracy resulting from the PAZ series standalone was acceptable, but it highlighted the need for a denser time-series of data. The overall accuracy provided by eight PAZ images or by eight Sentinel-1 images was below 60%. The fusion of both sets of eight images improved the overall accuracy by more than 10%. In addition, the exploitation of the whole Sentinel-1 series, with many more observations (up to 40 in the same temporal window) improved the results, reaching an overall accuracy around 76%. This overall performance was similar to that obtained by the joint use of all the available images of the two frequency bands (C and X). Full article
(This article belongs to the Special Issue Artificial Intelligence for SAR Applications in Environmental Monitoring)
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