Special Issue "The Future of Remote Sensing: Harnessing the Data Revolution"

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

Deadline for manuscript submissions: 15 February 2022.

Special Issue Editor

Prof. Alexandru Isar
E-Mail Website
Guest Editor
Politehnica University Timișoara/Electronics Telecommunications and Information Technologies Faculty, 2 Bd. V. Pârvan, 300223, Timișoara, Romania
Interests: wavelets; detection and estimation; satellite image processing; data mining; radar and sonar

Special Issue Information

Dear Colleagues,

The rapid advancement and proliferation of remote sensing technology has led to widespread recognition of its potential to improve the efficiency, reliability, and safety of inspection and monitoring.

The progress of space agencies and research institutes, the advancements in electronics, signal and image processing and computer science, the increasing of number and sensors’ diversity produce an exponential growth of the remote sensing data acquired nowadays. Techniques where a large group of individuals, having mobile devices capable of sensing and computing, collectively share data, as for example crowdsensing, contribute significantly to this amount increasing, named data revolution.

Due to the explosive developments in sensing, geospatial sensors started to produce an increasing amount of data, and soon Big Data have become a reality. Cloud computing has gained dominance and nearly unlimited processing and storage capacity are offered. Very large computers are also widely available, providing the base for massive processing. It is important to emphasize that the real potential of harnessing data revolution is the capability to extract additional information that has not been feasible in the past.

We would like to invite you to submit articles about your recent research with respect to the following or similar topics.

New Sensors:

Laser, Earth Polychromatic Imaging Camera, for Visual Odometry, for ka-band altimetry,…

Sensors integration:

               Data fusion;
               Multispectral remote sensing,
               Combination of large, small and micro-satellites...

Big data,

               Parallel computing,
               Machine learning perspective:
               Data mining, multi-criteria decision-making, multivariate models for prediction,….

Cloud computing,

               Data Integration,
               Data Organization and Management,
               Programming Technologies for Data Processing.

New applications,

               Augmented reality (AR) and virtual reality (VR) systems,
               Improved bathymetric mapping of coasts and lakes,
               Massive datasets for understanding the big problems facing society,
               Changes detection,
               Events tracking,
               Improving localization in urban canyons,
               GNSS-reflectometry,
               Satellite based augmentation systems…

New algorithms,

               for simultaneous localization and mapping (SLAM),
               for data and image processing…

Prof. Alexandru Isar
Guest Editor

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

  • sensors integration
  • data revolution
  • big data
  • machine learning
  • data mining
  • cloud computing
  • SAR
  • SONAR
  • LIDAR
  • satellite based augmentation systems

Published Papers (3 papers)

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Research

Article
Hyperanalytic Wavelet-Based Robust Edge Detection
Remote Sens. 2021, 13(15), 2888; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13152888 - 23 Jul 2021
Viewed by 316
Abstract
The imperfections of image acquisition systems produce noise. The majority of edge detectors, including gradient-based edge detectors, are sensitive to noise. To reduce this sensitivity, the first step of some edge detectors’ algorithms, such as the Canny’s edge detector, is the filtering of [...] Read more.
The imperfections of image acquisition systems produce noise. The majority of edge detectors, including gradient-based edge detectors, are sensitive to noise. To reduce this sensitivity, the first step of some edge detectors’ algorithms, such as the Canny’s edge detector, is the filtering of acquired images with a Gaussian filter. We show experimentally that this filtering is not sufficient in case of strong Additive White Gaussian or multiplicative speckle noise, because the remaining grains of noise produce false edges. The aim of this paper is to improve edge detection robustness against Gaussian and speckle noise by preceding the Canny’s edge detector with a new type of denoising system. We propose a two-stage denoising system acting in the Hyperanalytic Wavelet Transform Domain. The results obtained in applying the proposed edge detection method outperform state-of-the-art edge detection results from the literature. Full article
(This article belongs to the Special Issue The Future of Remote Sensing: Harnessing the Data Revolution)
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Article
GNSS Localization in Constraint Environment by Image Fusing Techniques
Remote Sens. 2021, 13(10), 2021; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13102021 - 20 May 2021
Cited by 1 | Viewed by 700
Abstract
Satellite localization often suffers in terms of accuracy due to various reasons. One possible source of errors is represented by the lack of means to eliminate Non-Line-of-Sight satellite-related data. We propose here a method for fusing existing data with new information, extracted by [...] Read more.
Satellite localization often suffers in terms of accuracy due to various reasons. One possible source of errors is represented by the lack of means to eliminate Non-Line-of-Sight satellite-related data. We propose here a method for fusing existing data with new information, extracted by using roof-mounted cameras and adequate image processing algorithms. The roof-mounted camera is used to robustly segment the sky regions. The localization approach can benefit from this new information as it offers a way of excluding the Non-Line-of-Sight satellites. The output of the camera module is a probability map. One can easily decide which satellites should not be used for localization, by manipulating this probability map. Our approach is validated by extensive tests, which demonstrate the improvement of the localization itself (Horizontal Positioning Error reduction) and a moderate degradation of Horizontal Protection Level due to the Dilution of Precision phenomenon, which appears as a consequence of the reduction of the satellites’ number used for localization. Full article
(This article belongs to the Special Issue The Future of Remote Sensing: Harnessing the Data Revolution)
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Article
Selection of Lee Filter Window Size Based on Despeckling Efficiency Prediction for Sentinel SAR Images
Remote Sens. 2021, 13(10), 1887; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13101887 - 12 May 2021
Viewed by 461
Abstract
Radar imaging has many advantages. Meanwhile, SAR images suffer from a noise-like phenomenon called speckle. Many despeckling methods have been proposed to date but there is still no common opinion as to what the best filter is and/or what are its parameters (window [...] Read more.
Radar imaging has many advantages. Meanwhile, SAR images suffer from a noise-like phenomenon called speckle. Many despeckling methods have been proposed to date but there is still no common opinion as to what the best filter is and/or what are its parameters (window or block size, thresholds, etc.). The local statistic Lee filter is one of the most popular and best-known despeckling techniques in radar image processing. Using this filter and Sentinel-1 images as a case study, we show how filter parameters, namely scanning window size, can be selected for a given image based on filter efficiency prediction. Such a prediction can be carried out using a set of input parameters that can be easily and quickly calculated and employing a trained neural network that allows determining one or several criteria of filtering efficiency with high accuracy. The statistical analysis of the obtained results is carried out. This characterizes improvements due to the adaptive selection of the filter window size, both potential and based on prediction. We also analyzed what happens if, due to prediction errors, erroneous decisions are undertaken. Examples for simulated and real-life images are presented. Full article
(This article belongs to the Special Issue The Future of Remote Sensing: Harnessing the Data Revolution)
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