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The Future of Remote Sensing: Harnessing the Data Revolution

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

Deadline for manuscript submissions: closed (15 February 2022) | Viewed by 46771

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Special Issue Editor

Prof. Alexandru Isar

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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 Issues, Collections and Topics in MDPI journals

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 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

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

Published Papers (7 papers)

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Research

Jump to: Review

14 pages, 5654 KiB

Open AccessArticle

Estimating Tree Defects with Point Clouds Developed from Active and Passive Sensors

by Carli J. Morgan, Matthew Powers and Bogdan M. Strimbu

Remote Sens. 2022, 14(8), 1938; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14081938 - 17 Apr 2022

Cited by 3 | Viewed by 2089

Abstract

Traditional inventories require large investments of resources and a trained workforce to measure tree sizes and characteristics that affect wood quality and value, such as the presence of defects and damages. Handheld light detection and ranging (LiDAR) and photogrammetric point clouds developed using Structure from Motion (SfM) algorithms achieved promising results in tree detection and dimensional measurements. However, few studies have utilized handheld LiDAR or SfM to assess tree defects or damages. We used a Samsung Galaxy S7 smartphone camera to photograph trees and create digital models using SfM, and a handheld GeoSLAM Zeb Horizon to create LiDAR point cloud models of some of the main tree species from the Pacific Northwest. We compared measurements of damage count and damage length obtained from handheld LiDAR, SfM photogrammetry, and traditional field methods using linear mixed-effects models. The field method recorded nearly twice as many damages per tree as the handheld LiDAR and SfM methods, but there was no evidence that damage length measurements varied between the three survey methods. Lower damage counts derived from LiDAR and SfM were likely driven by the limited point cloud reconstructions of the upper stems, as usable tree heights were achieved, on average, at 13.6 m for LiDAR and 9.3 m for SfM, even though mean field-measured tree heights was 31.2 m. Our results suggest that handheld LiDAR and SfM approaches show potential for detection and measurement of tree damages, at least on the lower stem. Full article

(This article belongs to the Special Issue The Future of Remote Sensing: Harnessing the Data Revolution)

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29 pages, 45151 KiB

Open AccessArticle

On Spectral-Spatial Classification of Hyperspectral Images Using Image Denoising and Enhancement Techniques, Wavelet Transforms and Controlled Data Set Partitioning

by Andreia Valentina Miclea, Romulus Mircea Terebes, Serban Meza and Mihaela Cislariu

Remote Sens. 2022, 14(6), 1475; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14061475 - 18 Mar 2022

Cited by 10 | Viewed by 2321

Abstract

Obtaining relevant classification results for hyperspectral images depends on the quality of the data and the proposed selection of the samples and descriptors for the training and testing phases. We propose a hyperspectral image classification machine learning framework based on image processing techniques for denoising and enhancement and a parallel approach for the feature extraction step. This parallel approach is designed to extract the features by employing the wavelet transform in the spectral domain, and by using Local Binary Patterns to capture the texture-like information linked to the geometry of the scene in the spatial domain. The spectral and spatial features are concatenated for a Support Vector Machine-based supervised classifier. For the experimental validation, we propose a controlled sampling approach that ensures the independence of the selected samples for the training data set, respectively the testing data set, offering unbiased performance results. We argue that a random selection applied on the hyperspectral dataset to separate the samples for the learning and testing phases can cause overlapping between the two datasets, leading to biased classification results. The proposed approach, with the controlled sampling strategy, tested on three public datasets, Indian Pines, Salinas and Pavia University, provides good performance results. Full article

(This article belongs to the Special Issue The Future of Remote Sensing: Harnessing the Data Revolution)

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19 pages, 5298 KiB

Open AccessArticle

Use Remote Sensing and Machine Learning to Study the Changes of Broad-Leaved Forest Biomass and Their Climate Driving Forces in Nature Reserves of Northern Subtropics

by Zhibin Sun, Wenqi Qian, Qingfeng Huang, Haiyan Lv, Dagui Yu, Qiangxin Ou, Haomiao Lu and Xuehai Tang

Remote Sens. 2022, 14(5), 1066; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14051066 - 22 Feb 2022

Cited by 8 | Viewed by 2399

Abstract

Forest is the largest vegetation carbon pool in the global terrestrial ecosystem. The spatial distribution and change of forest biomass are of importance to reveal the surface spatial variation and driving factors, to analyze and evaluate forest productivity, and to evaluate ecological function of forest. In this study, broad-leaved forests located in a typical state nature reserve in northern subtropics were selected as the study area. Based on ground survey data and high-resolution remote sensing images, three machine learning models were used to identify the best remote sensing quantitative inversion model of forest biomass. The biomass of broad-leaved forest with 30-m resolution in the study area from 1998 to 2016 was estimated by using the best model about every two years. With the estimated biomass, multiple leading factors to cause biomass temporal change were then identified from dozens of remote sensing factors by investigating their nonlinear correlations. Our results showed that the artificial neural network (ANN) model was the best (R² = 0.8742) among the three, and its accuracy was also much higher than that of the traditional linear or nonlinear models. The mean biomass of the broad-leaved forest in the study area from 1998 to 2016 ranged from 90 to 145 Mg ha⁻¹, showing an obvious temporal variation. Instead of biomass, biomass change (BC) was studied further in this research. Significant correlations were found between BC in broad-leaved forest and three climate factors, including average daily maximum surface temperature, maximum precipitation, and maximum mean temperature. It was also found that BC has a strong correlation with the biomass at the previous time (i.e., two years ago). Those quantitative correlations were used to construct a linear model of BC with high accuracy (R² = 0.8873), providing a new way to estimate the biomass change of two years later based on the observations of current biomass and the three climate factors. Full article

(This article belongs to the Special Issue The Future of Remote Sensing: Harnessing the Data Revolution)

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32 pages, 8284 KiB

Open AccessArticle

Hyperanalytic Wavelet-Based Robust Edge Detection

by Alexandru Isar, Corina Nafornita and Georgiana Magu

Remote Sens. 2021, 13(15), 2888; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13152888 - 23 Jul 2021

Cited by 9 | Viewed by 2200

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 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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19 pages, 7591 KiB

Open AccessArticle

GNSS Localization in Constraint Environment by Image Fusing Techniques

by Ciprian David, Corina Nafornita, Vasile Gui, Andrei Campeanu, Guillaume Carrie and Michel Monnerat

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 2207

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 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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Graphical abstract

28 pages, 14284 KiB

Open AccessArticle

Selection of Lee Filter Window Size Based on Despeckling Efficiency Prediction for Sentinel SAR Images

by Oleksii Rubel, Vladimir Lukin, Andrii Rubel and Karen Egiazarian

Remote Sens. 2021, 13(10), 1887; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13101887 - 12 May 2021

Cited by 21 | Viewed by 4532

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 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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Review

Jump to: Research

110 pages, 11090 KiB

Open AccessEditor’s ChoiceReview

Google Earth Engine and Artificial Intelligence (AI): A Comprehensive Review

by Liping Yang, Joshua Driscol, Sarigai Sarigai, Qiusheng Wu, Haifei Chen and Christopher D. Lippitt

Remote Sens. 2022, 14(14), 3253; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14143253 - 06 Jul 2022

Cited by 60 | Viewed by 28096

Abstract

Remote sensing (RS) plays an important role gathering data in many critical domains (e.g., global climate change, risk assessment and vulnerability reduction of natural hazards, resilience of ecosystems, and urban planning). Retrieving, managing, and analyzing large amounts of RS imagery poses substantial challenges. Google Earth Engine (GEE) provides a scalable, cloud-based, geospatial retrieval and processing platform. GEE also provides access to the vast majority of freely available, public, multi-temporal RS data and offers free cloud-based computational power for geospatial data analysis. Artificial intelligence (AI) methods are a critical enabling technology to automating the interpretation of RS imagery, particularly on object-based domains, so the integration of AI methods into GEE represents a promising path towards operationalizing automated RS-based monitoring programs. In this article, we provide a systematic review of relevant literature to identify recent research that incorporates AI methods in GEE. We then discuss some of the major challenges of integrating GEE and AI and identify several priorities for future research. We developed an interactive web application designed to allow readers to intuitively and dynamically review the publications included in this literature review. Full article

(This article belongs to the Special Issue The Future of Remote Sensing: Harnessing the Data Revolution)

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