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SAR Processing in Urban Planning
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SAR Processing in Urban Planning

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

Deadline for manuscript submissions: 10 October 2024 | Viewed by 7234

Special Issue Editors

Dr. Georgia Koukiou

E-Mail Website1 Website2
Guest Editor
Electronics Laboratory (ELLAB), Physics Department, University of Patras, 26504 Rio, Greece
Interests: image processing; signal processing; machine learning; signal, image and video processing; remote sensing
Prof. Dr. Vassilis Anastassopoulos

E-Mail Website
Guest Editor
Department of Physics, University of Patras, Patras, Greece
Interests: signal and image processing; pattern recognition; remote sensing; information fusion
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Urban areas are nowadays occupied by more than half the Earths’ population. This is due to migration mainly towards the developing countries. Rapid urban development has resulted in environmental problems linked to unsustainable transport, housing, waste, energy, and land use management. This necessitates the emergent solutions for self-sustaining and healthier communities. Simultaneously, the urban health risk is different across areas with different socioeconomic status. Urban planning by means of modern tools and intelligent methods is of great importance.

SAR properties are very important in analyzing earth surface and obtaining all necessary information for urban areas understanding and planning:

  • The ability to acquire images day and night, regardless of the weather conditions.
  • Measuring polarimetric properties of each background pixel can reveal geometric and polarization properties which are related to surface orientation, reflectance, and substance composition.
  • The measurement of the phase of the electric field allows for the implementation of specific techniques, such as 3D interferometry (InSAR), differential interferometry (DInSAR), or tomography.

The submitted papers should:

Use multimodal methods incorporating SAR images and fusing the available distributed information by spatial and/or temporal data processing methods. These methods could incorporate machine learning approaches from feature extraction, classification, neural networks, and pattern recognition.

Multitemporal analysis employing 3D methods and leading to interferometry and/or, tomography, and elevation models. 

The main applications are expected to be super-resolution, polarization categorization, urban sprawl, anthropic activities, subsidence. We aim to gain an understanding of urban and artificialized environments, their evolution, and monitoring indicator.

Dr. Georgia Koukiou
Prof. Dr. Vassilis Anastassopoulos
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 polarimetry, interferometry, 3D models
  • machine learning applications in:
    • urban disaster management
    • urban Vegetation monitoring
    • urban Ecological infrastructure
    • urban cultural heritage and models
  • gradually changing environments
  • environmental health risk assessment
  • health indicators
  • water resources and sanitation in urban areas
  • urban groundwater system
  • urban solid waste management

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Published Papers (5 papers)

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Research

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25 pages, 26371 KiB  
Article
Identifying Heterogeneity in SAR Data with New Test Statistics
by Alejandro C. Frery, Janeth Alpala and Abraão D. C. Nascimento
Remote Sens. 2024, 16(11), 1973; https://doi.org/10.3390/rs16111973 - 30 May 2024
Viewed by 733
Abstract
This paper presents a statistical approach to identify the underlying roughness characteristics in synthetic aperture radar (SAR) intensity data. The physical modeling of this kind of data allows the use of the Gamma distribution in the presence of fully developed speckle, i.e., when [...] Read more.
This paper presents a statistical approach to identify the underlying roughness characteristics in synthetic aperture radar (SAR) intensity data. The physical modeling of this kind of data allows the use of the Gamma distribution in the presence of fully developed speckle, i.e., when there are infinitely many independent backscatterers per resolution cell, and none dominates the return. Such areas are often called “homogeneous” or “textureless” regions. The GI0 distribution is also a widely accepted law for heterogeneous and extremely heterogeneous regions, i.e., areas where the fully developed speckle hypotheses do not hold. We propose three test statistics to distinguish between homogeneous and inhomogeneous regions, i.e., between gamma and GI0 distributed data, both with a known number of looks. The first test statistic uses a bootstrapped non-parametric estimator of Shannon entropy, providing a robust assessment in uncertain distributional assumptions. The second test uses the classical coefficient of variation (CV). The third test uses an alternative form of estimating the CV based on the ratio of the mean absolute deviation from the median to the median. We apply our test statistic to create maps of p-values for the homogeneity hypothesis. Finally, we show that our proposal, the entropy-based test, outperforms existing methods, such as the classical CV and its alternative variant, in identifying heterogeneity when applied to both simulated and actual data. Full article
(This article belongs to the Special Issue SAR Processing in Urban Planning)
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18 pages, 15059 KiB  
Article
PolSAR Image Classification by Introducing POA and HA Variances
by Zeying Lan, Yang Liu, Jianhua He and Xin Hu
Remote Sens. 2023, 15(18), 4464; https://doi.org/10.3390/rs15184464 - 11 Sep 2023
Viewed by 1022
Abstract
A polarimetric synthetic aperture radar (PolSAR) has great potential in ground target classification. However, current methods experience difficulties in separating forests and buildings, especially oriented buildings. To address this issue, inspired by the three-component decomposition method, multiple new scattering models were proposed to [...] Read more.
A polarimetric synthetic aperture radar (PolSAR) has great potential in ground target classification. However, current methods experience difficulties in separating forests and buildings, especially oriented buildings. To address this issue, inspired by the three-component decomposition method, multiple new scattering models were proposed to describe the difference between forest scattering and building scattering. However, this problem cannot effectively be solved with scattering power alone since HV polarization records significant scattering powers from building areas that are similar to vegetation. Therefore, in this study, two new parameters, the polarization orientation angle (POA) variance and helix angle (HA) variance, were defined to describe the distributions of buildings and forests. By combining scattering power with POA and HA variances, the random forest algorithm was used to conduct the land cover classification, focusing on distinguishing between forests and oriented buildings. Finally, the C- and L-band polarimetric SAR data acquired by the GF-3, ALOS1 PALSAR, and SAOCOM systems were selected to test the proposed method. The results indicate that it is feasible to improve PolSAR classification accuracy by introducing polarimetric parameters. Quantitatively, the classification accuracies increased by 23.78%, 10.80%, and 12.97% for the ALOS1 PALSAR, GF-3, and SAOCOM data, respectively. Full article
(This article belongs to the Special Issue SAR Processing in Urban Planning)
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15 pages, 7818 KiB  
Article
Measuring Vertical Urban Growth of Patna Urban Agglomeration Using Persistent Scatterer Interferometry SAR (PSInSAR) Remote Sensing
by Aniket Prakash, Diksha and Amit Kumar
Remote Sens. 2023, 15(14), 3687; https://doi.org/10.3390/rs15143687 - 24 Jul 2023
Viewed by 1845
Abstract
In the present study, the vertical and horizontal growth of Patna Urban Agglomeration was evaluated using the Persistent Scatterer Interferometry Synthetic Aperture Radar (PSInSAR) technique during 2015–2018. The vertical urban growth assessment of the city landscape was assessed using microwave time series (30 [...] Read more.
In the present study, the vertical and horizontal growth of Patna Urban Agglomeration was evaluated using the Persistent Scatterer Interferometry Synthetic Aperture Radar (PSInSAR) technique during 2015–2018. The vertical urban growth assessment of the city landscape was assessed using microwave time series (30 temporal) datasets of Single Look Complex (SLC) Sentinel-1A interferometric Synthetic Aperture Radar using SARPROZ software (ver. 2020). This study demonstrated that peripheral city regions experienced higher vertical growth (~4 m year−1) compared to the city core regions, owing to higher urban development opportunities leading to significant land use alterations, the development of high-rise buildings, and infrastructural development. While the city core of Patna observed an infill and densification process, as it was already saturated and highly densified. The rapidly urbanizing city in the developing region witnessed a considerable horizontal urban expansion as estimated through the normalized difference index for built-up areas (NDIB) and speckle divergence (SD) using optical Sentinel 2A and microwave Sentinel-1A ground range detected (GRD) satellite data, respectively. The speckle divergence-based method exhibited high urban growth (net growth of 11.28 km2) with moderate urban infill during 2015–2018 and reported a higher accuracy as compared to NDIB. This study highlights the application of SAR remote sensing for precise urban area delineation and temporal monitoring of urban growth considering horizontal and vertical expansion through processing a long series of InSAR datasets that provide valuable information for informed decision-making and support the development of sustainable and resilient cities. Full article
(This article belongs to the Special Issue SAR Processing in Urban Planning)
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18 pages, 28374 KiB  
Article
Monitoring Building Activity by Persistent Scatterer Interferometry
by Vasilis Letsios, Ioannis Faraslis and Demetris Stathakis
Remote Sens. 2023, 15(4), 950; https://doi.org/10.3390/rs15040950 - 9 Feb 2023
Cited by 4 | Viewed by 1786
Abstract
In many countries globally, information for new buildings is either scarce or incomplete. In an effort to bridge this information gap an approach based on public domain synthetic aperture radar (SAR) satellite data is introduced. The method is based on the persistent scatterer [...] Read more.
In many countries globally, information for new buildings is either scarce or incomplete. In an effort to bridge this information gap an approach based on public domain synthetic aperture radar (SAR) satellite data is introduced. The method is based on the persistent scatterer interferometry (PSI) technique in order to detect newly constructed buildings and estimate their heights in a selected case study area in Athens, Greece. The overall objective is to derive timely spatial information for building activity. A key concept of the approach is the residual height, i.e., the difference in height of each point compared to a reference point that is suitably selected. The data used are acquired by the Sentinel-1 satellite. Both ascending and descending orbits and dual polarizations (VV/VH) are used. The results show that as much as 70% of new buildings can be detected at a vertical accuracy approximately of 2.5 m, which is sufficient to determine the number of stories per building. Overall, the proposed method can provide an efficient insight regarding building activity and provide a significant information layer for urban studies in a rapidly changing world. Full article
(This article belongs to the Special Issue SAR Processing in Urban Planning)
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Review

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38 pages, 2284 KiB  
Review
SAR Features and Techniques for Urban Planning—A Review
by Georgia Koukiou
Remote Sens. 2024, 16(11), 1923; https://doi.org/10.3390/rs16111923 - 27 May 2024
Viewed by 1028
Abstract
Urban planning has, in recent years, been significantly assisted by remote sensing data. The data and techniques that are used are very diverse and are available to government agencies as well as to private companies that are involved in planning urban and peri-urban [...] Read more.
Urban planning has, in recent years, been significantly assisted by remote sensing data. The data and techniques that are used are very diverse and are available to government agencies as well as to private companies that are involved in planning urban and peri-urban areas. Synthetic aperture radar data are particularly important since they provide information on the geometric and electrical characteristics of ground objects and, at the same time, are unaffected by sunlight (day–night) and cloud cover. SAR data are usually combined with optical data (fusion) in order to increase the reliability of the terrain information. Most of the existing relative classification methods have been reviewed. New techniques that have been developed use decorrelation and interferometry to record changes on the Earth’s surface. Texture-based features, such as Markov random fields and co-occurrence matrices, are employed, among others, for terrain classification. Furthermore, target geometrical features are used for the same purpose. Among the innovative works presented in this manuscript are those dealing with tomographic SAR imaging for creating digital elevation models in urban areas. Finally, tomographic techniques and digital elevation models can render three-dimensional representations for a much better understanding of the urban region. The above-mentioned sources of information are integrated into geographic information systems, making them more intelligent. In this work, most of the previous techniques and methods are reviewed, and selected papers are highlighted in order for the reader-researcher to have a complete picture of the use of SAR in urban planning. Full article
(This article belongs to the Special Issue SAR Processing in Urban Planning)
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