Special Issue "Data Fusion for Urban Applications"

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

Deadline for manuscript submissions: 31 October 2021.

Special Issue Editors

Dr. Stefan Auer
E-Mail Website
Guest Editor
German Aerospace Center (DLR), Remote Sensing Technology Institute, Muenchener Strasse 20, 82234 Wessling-Oberpfaffenhofen, Germany
Interests: Remote Sensing; Image Analysis; Interpretation; Data Fusion; Simulation
PD Dr. Michael Schmitt
E-Mail Website
Guest Editor
Signal Processing in Earth Observation, Technical University of Munich, Munich, 80333, Germany
Interests: Remote Sensing; Data Fusion; Machine Learning; Geospatial Data Science
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Remote Sensing applications for urban areas are of major importance as the majority of human population is concentrated in these regions. Looking at reported literature, data from different sensors have been acquired, e.g., in order to characterize the nature of city areas, monitor urban development over time or detect changes after unexpected events. In this context, the unification of information from multimodal sensors in urban applications has always been very welcome, but so far hard to solve. On the one hand, finding appropriate strategies for combining the complementary information is difficult. On the other hand, the assignment of multi-modal information to entities of urban scenes is often not unambiguous. An increase of the spatial resolution of image data does not necessarily help but even tightens the conditions for useful solutions.

This Special Issue is devoted to strategies and methods for fusing multi-modal data in the context of urban remote sensing. As a general guideline, complementary sources should be combined in order to gain improved information about urban areas.   

Submitting authors are encouraged to address one of the following topics in the context of remote sensing data (not exclusively):

  • Enhancement of urban applications through exploitation of complementary information provided by data from multiple sensors, multiple sources and multi-temporal acquisitions;
  • Integration of external prior knowledge into urban remote sensing;
  • Fusion of information from remote sensing and non-typical Earth observation data sources (terrestrial data, data from social media, etc.) for improved understanding of urban problems;
  • 2-D, 3-D and multi-dimensional data fusion for urban analysis;
  • Multi-view fusion for exploiting different perspectives on urban elements;
  • Data fusion for urban tasks conducted on data level, feature level, or decision level;
  • Urban applications on different resolution levels (spatial, spectral, temporal).

Dr. Stefan Auer
PD Dr. Michael Schmitt
Dr. Naoto Yokoya
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 papers will be 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 2400 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

  • Data fusion
  • Image fusion
  • Multi-sensor fusion
  • Multi-source fusion
  • Urban applications
  • City monitoring
  • Change detection
  • Multi-resolution data
  • Multi-temporal data
  • Multi-spectral data
  • Accuracy assessment

Published Papers (3 papers)

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Research

Open AccessArticle
An Improved Index for Urban Population Distribution Mapping Based on Nighttime Lights (DMSP-OLS) Data: An Experiment in Riyadh Province, Saudi Arabia
Remote Sens. 2021, 13(6), 1171; https://doi.org/10.3390/rs13061171 - 19 Mar 2021
Viewed by 338
Abstract
Knowledge of the spatial pattern of the population is important. Census population data provide insufficient spatial information because they are released only for large geographic areas. Nighttime light (NTL) data have been utilized widely as an effective proxy for population mapping. However, the [...] Read more.
Knowledge of the spatial pattern of the population is important. Census population data provide insufficient spatial information because they are released only for large geographic areas. Nighttime light (NTL) data have been utilized widely as an effective proxy for population mapping. However, the well-reported challenges of pixel overglow and saturation influence the applicability of the Defense Meteorological Program Operational Line-Scan System (DMSP-OLS) for accurate population mapping. This paper integrates three remotely sensed information sources, DMSP-OLS, vegetation, and bare land areas, to develop a novel index called the Vegetation-Bare Adjusted NTL Index (VBANTLI) to overcome the uncertainties in the DMSP-OLS data. The VBANTLI was applied to Riyadh province to downscale governorate-level census population for 2004 and 2010 to a gridded surface of 1 km resolution. The experimental results confirmed that the VBANTLI significantly reduced the overglow and saturation effects compared to widely applied indices such as the Human Settlement Index (HSI), Vegetation Adjusted Normalized Urban Index (VANUI), and radiance-calibrated NTL (RCNTL). The correlation coefficient between the census population and the RCNTL (R = 0.99) and VBANTLI (R = 0.98) was larger than for the HSI (R = 0.14) and VANUI (R = 0.81) products. In addition, Model 5 (VBANTLI) was the most accurate model with R2 and mean relative error (MRE) values of 0.95% and 37%, respectively. Full article
(This article belongs to the Special Issue Data Fusion for Urban Applications)
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Open AccessArticle
An Optimized Filtering Method of Massive Interferometric SAR Data for Urban Areas by Online Tensor Decomposition
Remote Sens. 2020, 12(16), 2582; https://doi.org/10.3390/rs12162582 - 11 Aug 2020
Viewed by 817
Abstract
The filtering of multi-pass synthetic aperture radar interferometry (InSAR) stack data is a necessary preprocessing step utilized to improve the accuracy of the object-based three-dimensional information inversion in urban area. InSAR stack data is composed of multi-temporal homogeneous data, which is regarded as [...] Read more.
The filtering of multi-pass synthetic aperture radar interferometry (InSAR) stack data is a necessary preprocessing step utilized to improve the accuracy of the object-based three-dimensional information inversion in urban area. InSAR stack data is composed of multi-temporal homogeneous data, which is regarded as a third-order tensor. The InSAR tensor can be filtered by data fusion, i.e., tensor decomposition, and these filters keep balance in the noise elimination and the fringe details preservation, especially with abrupt fringe change, e.g., the edge of urban structures. However, tensor decomposition based on batch processing cannot deal with few newly acquired interferograms filtering directly. The filtering of dynamic InSAR tensor is the inevitable challenge when processing InSAR stack data, where dynamic InSAR tensor denotes the size of InSAR tensor increases continuously due to the acquisition of new interferograms. Therefore, based on the online CANDECAMP/PARAFAC (CP) decomposition, we propose an online filter to fuse data and process the dynamic InSAR tensor, named OLCP-InSAR, which performs well especially for the urban area. In this method, CP rank is utilized to measure the tensor sparsity, which can maintain the structural features of the InSAR tensor. Additionally, CP rank estimation is applied as an important step to improve the robustness of Online CP decomposition - InSAR(OLCP-InSAR). Importing CP rank and outlier’s position as prior information, the filter fuses the noisy interferograms and decomposes the InSAR tensor to acquire the low rank information, i.e., filtered result. Moreover, this method can not only operate on tensor model, but also efficiently filter the new acquired interferogram as matrix model with the assistance of chosen low rank information. Compared with other tensor-based filters, e.g., high order robust principal component analysis (HoRPCA) and Kronecker-basis-representation multi-pass SAR interferometry (KBR-InSAR), and the widespread traditional filters operating on a single interferometric pair, e.g., Goldstein, non-local synthetic aperture radar (NL-SAR), non-local InSAR (NL-InSAR), and InSAR nonlocal block-matching 3-D (InSAR-BM3D), the effectiveness and robustness of OLCP-InSAR are proved in simulated and real InSAR stack data. Especially, OLCP-InSAR can maintain the fringe details at the regular building top with high noise intensity and high outlier ratio. Full article
(This article belongs to the Special Issue Data Fusion for Urban Applications)
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Open AccessArticle
A Survey of Change Detection Methods Based on Remote Sensing Images for Multi-Source and Multi-Objective Scenarios
Remote Sens. 2020, 12(15), 2460; https://doi.org/10.3390/rs12152460 - 31 Jul 2020
Cited by 2 | Viewed by 1354
Abstract
Quantities of multi-temporal remote sensing (RS) images create favorable conditions for exploring the urban change in the long term. However, diverse multi-source features and change patterns bring challenges to the change detection in urban cases. In order to sort out the development venation [...] Read more.
Quantities of multi-temporal remote sensing (RS) images create favorable conditions for exploring the urban change in the long term. However, diverse multi-source features and change patterns bring challenges to the change detection in urban cases. In order to sort out the development venation of urban change detection, we make an observation of the literatures on change detection in the last five years, which focuses on the disparate multi-source RS images and multi-objective scenarios determined according to scene category. Based on the survey, a general change detection framework, including change information extraction, data fusion, and analysis of multi-objective scenarios modules, is summarized. Owing to the attributes of input RS images affect the technical selection of each module, data characteristics and application domains across different categories of RS images are discussed firstly. On this basis, not only the evolution process and relationship of the representative solutions are elaborated in the module description, through emphasizing the feasibility of fusing diverse data and the manifold application scenarios, we also advocate a complete change detection pipeline. At the end of the paper, we conclude the current development situation and put forward possible research direction of urban change detection, in the hope of providing insights to the following research. Full article
(This article belongs to the Special Issue Data Fusion for Urban Applications)
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