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Special Issue "Computational Intelligence in Remote Sensing"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (1 June 2019).

Special Issue Editors

Dr. Manuel Graña
Website
Guest Editor
Universidad del Pais Vasco, Computational Intelligence Group, San Sebastian, Spain
Interests: hyperspectral image analysis; computational intelligence; medical imaging
Special Issues and Collections in MDPI journals
Prof. Dr. Michal Wozniak
Website
Guest Editor
Department of Systems and Computer Networks, Wroclaw University of Science and Technology, 50-370 Wrocław, Poland
Interests: hyperspectral image analysis; data drift analysis; health care data analysis
Dr. Sebastian Rios
Website
Guest Editor
Ingeniero Civil Industrial, Universidad de Chile, 0324234 Santiago, Chile
Interests: big data; data science; remote sensing; social network analysis
Dr. Javier De Lope Asiaín
Website
Guest Editor
Department of Artificial Intelligence, Universidad Politecnica de Madrid, 28040 Madrid, Spain
Interests: image processing; robotics; machine vision

Special Issue Information

Dear Colleagues,

The appearance of new and more powerful remote sensing technologies has produced a surge of remote sensing data to be processed for a variety of applications, such as precision agriculture, biomass estimation, fire prevention, forest management, environmental monitoring. Computational intelligence tools for data processing are increasingly being used for pre-processing, enhancement, classification, and construction of thematic maps, change detection, target detection, subpixel resolution analysis, and other general processes. The rediscovery of artificial neural networks with the resurgence of deep learning approaches has injected new vitality in various of the research fields that deal with remote sensing data analysis. Of paramount importance for the development of reproducible science is the availability of data repositories and open source codes that may be used by researchers across the world to confirm or refute claimed results. The open source code has boosted many data science applications, allowing the researchers to work on high-level developments and providing a unified set of tools. In this Special Issue, we emphasize the availability of data and open-source solutions, so that papers may be devoted to describing and sharing such platforms. Besides, we are interested in innovative computational intelligence techniques and algorithms contributing to the state of the art, including deep learning architectures, new bio-inspired optimization techniques, and fuzzy reasoning techniques. We also look for studies presenting techniques dealing with the changing, non-stationary nature of the data considered in time, a main challenge faced by the new generation of remote sensing data analysis tools. Finally, papers describing techniques exploiting various data sources, such as multimodal image fusion or other, are welcome.

Prof. Manuel Graña
Prof. Michal Wozniak
Dr. Sebastian Rios
Dr. Javier de Lope Asiaín
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. Sensors 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 2000 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

  • Remote sensing data repositories
  • Open-source solutions and platforms
  • Deep learning and machine learning techniques
  • Change detection and data drift
  • Classification, target detection, subpixel resolution detection
  • Multispectral and hyperspectral images, synthetic aperture
  • radar, LIDAR
  • Multimodal image fusion
  • Super-resolution
  • Applications: precision agriculture, environmental monitoring

Published Papers (12 papers)

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Editorial

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Open AccessEditorial
Computational Intelligence in Remote Sensing: An Editorial
Sensors 2020, 20(3), 633; https://doi.org/10.3390/s20030633 - 23 Jan 2020
Abstract
Computational intelligence is a very active and fruitful research of artificial intelligence with a broad spectrum of applications. Remote sensing data has been a salient field of application of computational intelligence algorithms, both for the exploitation of the data and for the research/ [...] Read more.
Computational intelligence is a very active and fruitful research of artificial intelligence with a broad spectrum of applications. Remote sensing data has been a salient field of application of computational intelligence algorithms, both for the exploitation of the data and for the research/ development of new data analysis tools. In this editorial paper we provide the setting of the special issue “Computational Intelligence in Remote Sensing” and an overview of the published papers. The 11 accepted and published papers cover a wide spectrum of applications and computational tools that we try to summarize and put in perspective in this editorial paper. Full article
(This article belongs to the Special Issue Computational Intelligence in Remote Sensing)

Research

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Open AccessArticle
Indoor Crowd 3D Localization in Big Buildings from Wi-Fi Access Anonymous Data
Sensors 2019, 19(19), 4211; https://doi.org/10.3390/s19194211 - 27 Sep 2019
Cited by 2
Abstract
Indoor crowd localization and counting in big public buildings pose problems of infrastructure deployment, signal processing, and privacy. Conventional approaches based on optical cameras, either in the visible or infrared range, received signal strength in wireless networks, sound or chemical sensing in sensor [...] Read more.
Indoor crowd localization and counting in big public buildings pose problems of infrastructure deployment, signal processing, and privacy. Conventional approaches based on optical cameras, either in the visible or infrared range, received signal strength in wireless networks, sound or chemical sensing in sensor networks need careful calibration, noise removal, and sophisticated data processing to achieve results in limited scenarios. Moreover, personal data protection is a growing concern, so that detection methods that preserve the privacy of people are highly desirable. The aim of this paper is to provide a technique that may generate estimations of the localization of people in a big public building using anonymous data from already-deployed Wi-Fi infrastructure. We present a method applying geostatistical techniques to the access data acquired from Access Points (AP) in an open Wi-Fi network. Specifically, only the time series of the number of accesses per AP is required. Geostatistical methods produce a 3D high-quality spatial distribution representation of the people inside the building based on the interaction of their mobile devices with the APs. We report encouraging results obtained from data acquired at a building of Wroclaw University of Science and Technology. Full article
(This article belongs to the Special Issue Computational Intelligence in Remote Sensing)
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Open AccessArticle
Automatic and Fast Recognition of On-Road High-Emitting Vehicles Using an Optical Remote Sensing System
Sensors 2019, 19(16), 3540; https://doi.org/10.3390/s19163540 - 13 Aug 2019
Cited by 2
Abstract
Optical remote sensing systems (RSSs) for monitoring vehicle emissions can be installed on any road and provide non-contact on-road measurements, that allow law enforcement departments to monitor emissions of a large number of on-road vehicles. Although many studies in different research fields have [...] Read more.
Optical remote sensing systems (RSSs) for monitoring vehicle emissions can be installed on any road and provide non-contact on-road measurements, that allow law enforcement departments to monitor emissions of a large number of on-road vehicles. Although many studies in different research fields have been performed using RSSs, there has been little research on the automatic recognition of on-road high-emitting vehicles. In general, high-emitting vehicles and low-emitting vehicles are classified by fixed emission concentration cut-points, that lack a strict scientific basis, and the actual cut-points are sensitive to environmental factors, such as wind speed and direction, outdoor temperature, relative humidity, atmospheric pressure, and so on. Besides this issue, single instantaneous monitoring results from RSSs are easily affected by systematic and random errors, leading to unreliable results. This paper proposes a method to solve the above problems. The automatic and fast-recognition method for on-road high-emitting vehicles (AFR-OHV) is the first application of machine learning, combined with big data analysis for remote sensing monitoring of on-road high-emitting vehicles. The method constructs adaptively updates a clustering database using real-time collections of emission datasets from an RSS. Then, new vehicles, that pass through the RSS, are recognized rapidly by the nearest neighbor classifier, which is guided by a real-time updated clustering database. Experimental results, based on real data, including the Davies-Bouldin Index (DBI) and Dunn Validity Index (DVI), show that AFR-OHV provides faster convergence speed and better performance. Furthermore, it is not easily disturbed by outliers. Our classifier obtains high scores for Precision (PRE), Recall (REC), the Receiver Operator Characteristic (ROC), and the Area Under the Curve (AUC). The rates of different classifications of excessive emissions and self-adaptive cut-points are calculated automatically in order to provide references for law enforcement departments to establish evaluation criterion for on-road high-emitting vehicles, detected by the RSS. Full article
(This article belongs to the Special Issue Computational Intelligence in Remote Sensing)
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Open AccessArticle
PARSUC: A Parallel Subsampling-Based Method for Clustering Remote Sensing Big Data
Sensors 2019, 19(15), 3438; https://doi.org/10.3390/s19153438 - 05 Aug 2019
Cited by 1
Abstract
Remote sensing big data (RSBD) is generally characterized by huge volumes, diversity, and high dimensionality. Mining hidden information from RSBD for different applications imposes significant computational challenges. Clustering is an important data mining technique widely used in processing and analyzing remote sensing imagery. [...] Read more.
Remote sensing big data (RSBD) is generally characterized by huge volumes, diversity, and high dimensionality. Mining hidden information from RSBD for different applications imposes significant computational challenges. Clustering is an important data mining technique widely used in processing and analyzing remote sensing imagery. However, conventional clustering algorithms are designed for relatively small datasets. When applied to problems with RSBD, they are, in general, too slow or inefficient for practical use. In this paper, we proposed a parallel subsampling-based clustering (PARSUC) method for improving the performance of RSBD clustering in terms of both efficiency and accuracy. PARSUC leverages a novel subsampling-based data partitioning (SubDP) method to realize three-step parallel clustering, effectively solving the notable performance bottleneck of the existing parallel clustering algorithms; that is, they must cope with numerous repeated calculations to get a reasonable result. Furthermore, we propose a centroid filtering algorithm (CFA) to eliminate subsampling errors and to guarantee the accuracy of the clustering results. PARSUC was implemented on a Hadoop platform by using the MapReduce parallel model. Experiments conducted on massive remote sensing imageries with different sizes showed that PARSUC (1) provided much better accuracy than conventional remote sensing clustering algorithms in handling larger image data; (2) achieved notable scalability with increased computing nodes added; and (3) spent much less time than the existing parallel clustering algorithm in handling RSBD. Full article
(This article belongs to the Special Issue Computational Intelligence in Remote Sensing)
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Open AccessArticle
3D Convex Hull-Based Registration Method for Point Cloud Watermark Extraction
Sensors 2019, 19(15), 3268; https://doi.org/10.3390/s19153268 - 25 Jul 2019
Cited by 1
Abstract
Most 3D point cloud watermarking techniques apply Principal Component Analysis (PCA) to protect the watermark against affine transformation attacks. Unfortunately, they fail in the case of cropping and random point removal attacks. In this work, an alternative approach is proposed that solves these [...] Read more.
Most 3D point cloud watermarking techniques apply Principal Component Analysis (PCA) to protect the watermark against affine transformation attacks. Unfortunately, they fail in the case of cropping and random point removal attacks. In this work, an alternative approach is proposed that solves these issues efficiently. A point cloud registration technique is developed, based on a 3D convex hull. The scale and the initial rigid affine transformation between the watermarked and the original point cloud can be estimated in this way to obtain a coarse point cloud registration. An iterative closest point algorithm is performed after that to align the attacked watermarked point cloud to the original one completely. The watermark can then be extracted from the watermarked point cloud easily. The extensive experiments confirmed that the proposed approach resists the affine transformation, cropping, random point removal, and various combinations of these attacks. The most dangerous is an attack with noise that can be handled only to some extent. However, this issue is common to the other state-of-the-art approaches. Full article
(This article belongs to the Special Issue Computational Intelligence in Remote Sensing)
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Open AccessArticle
An Approach for the Customized High-Dimensional Segmentation of Remote Sensing Hyperspectral Images
Sensors 2019, 19(13), 2887; https://doi.org/10.3390/s19132887 - 29 Jun 2019
Cited by 2
Abstract
This paper addresses three problems in the field of hyperspectral image segmentation: the fact that the way an image must be segmented is related to what the user requires and the application; the lack and cost of appropriately labeled reference images; and, finally, [...] Read more.
This paper addresses three problems in the field of hyperspectral image segmentation: the fact that the way an image must be segmented is related to what the user requires and the application; the lack and cost of appropriately labeled reference images; and, finally, the information loss problem that arises in many algorithms when high dimensional images are projected onto lower dimensional spaces before starting the segmentation process. To address these issues, the Multi-Gradient based Cellular Automaton (MGCA) structure is proposed to segment multidimensional images without projecting them to lower dimensional spaces. The MGCA structure is coupled with an evolutionary algorithm (ECAS-II) in order to produce the transition rule sets required by MGCA segmenters. These sets are customized to specific segmentation needs as a function of a set of low dimensional training images in which the user expresses his segmentation requirements. Constructing high dimensional image segmenters from low dimensional training sets alleviates the problem of lack of labeled training images. These can be generated online based on a parametrization of the desired segmentation extracted from a set of examples. The strategy has been tested in experiments carried out using synthetic and real hyperspectral images, and it has been compared to state-of-the-art segmentation approaches over benchmark images in the area of remote sensing hyperspectral imaging. Full article
(This article belongs to the Special Issue Computational Intelligence in Remote Sensing)
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Open AccessArticle
Logarithmic-Domain Array Interpolation for Improved Direction of Arrival Estimation in Automotive Radars
Sensors 2019, 19(10), 2410; https://doi.org/10.3390/s19102410 - 27 May 2019
Cited by 5
Abstract
In automotive radar systems, a limited number of antenna elements are used to estimate the angle of the target. Therefore, array interpolation techniques can be used for direction of arrival (DOA) estimation to achieve high angular resolution. In general, to generate interpolated array [...] Read more.
In automotive radar systems, a limited number of antenna elements are used to estimate the angle of the target. Therefore, array interpolation techniques can be used for direction of arrival (DOA) estimation to achieve high angular resolution. In general, to generate interpolated array elements from original array elements, the method of linear least squares (LLS) is used. When the LLS method is used, the amplitudes of the interpolated array elements may not be equivalent to those of the original array elements. In addition, through the transformation matrix obtained from the LLS method, the phases of the interpolated array elements are not precisely generated. Therefore, we propose an array transformation matrix that generates accurate phases for interpolated array elements to improve DOA estimation performance, while maintaining constant amplitudes of the array elements. Moreover, to enhance the effect of our interpolation method, a power calibration method for interpolated received signals is also proposed. Through the simulation, we confirm that the array interpolation accuracy and DOA estimation performance of the proposed method are improved compared to those of the conventional method. Moreover, the performance and effectiveness of our proposed method are also verified using data obtained from the commercial radar system. Because the proposed method exhibits better performance when applied to actual measurement data, it can be utilized in commercial automotive radar systems. Full article
(This article belongs to the Special Issue Computational Intelligence in Remote Sensing)
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Open AccessArticle
Eliminating the Effect of Image Border with Image Periodic Decomposition for Phase Correlation Based Remote Sensing Image Registration
Sensors 2019, 19(10), 2329; https://doi.org/10.3390/s19102329 - 20 May 2019
Cited by 2
Abstract
In the remote sensing community, accurate image registration is the prerequisite of the subsequent application of remote sensing images. Phase correlation based image registration has drawn extensive attention due to its high accuracy and high efficiency. However, when the Discrete Fourier Transform (DFT) [...] Read more.
In the remote sensing community, accurate image registration is the prerequisite of the subsequent application of remote sensing images. Phase correlation based image registration has drawn extensive attention due to its high accuracy and high efficiency. However, when the Discrete Fourier Transform (DFT) of an image is computed, the image is implicitly assumed to be periodic. In practical application, it is impossible to meet the periodic condition that opposite borders of an image are alike, and image always shows strong discontinuities across the frame border. The discontinuities cause a severe artifact in the Fourier Transform, namely the known cross structure composed of high energy coefficients along the axes. Here, this phenomenon was referred to as effect of image border. Even worse, the effect of image border corrupted its registration accuracy and success rate. Currently, the main solution is blurring out the border of the image by weighting window function on the reference and sensed image. However, the approach also inevitably filters out non-border information of an image. The existing understanding is that the design of window function should filter as little information as possible, which can improve the registration success rate and accuracy of methods based on phase correlation. In this paper, another approach of eliminating the effect of image border is proposed, namely decomposing the image into two images: one being the periodic image and the other the smooth image. Replacing the original image by the periodic one does not suffer from the effect on the image border when applying Fourier Transform. The smooth image is analogous to an error image, which has little information except at the border. Extensive experiments were carried out and showed that the novel algorithm of eliminating the image border can improve the success rate and accuracy of phase correlation based image registration in some certain cases. Additionally, we obtained a new understanding of the role of window function in eliminating the effect of image border, which is helpful for researchers to select the optimal method of eliminating the effect of image border to improve the registration success rate and accuracy. Full article
(This article belongs to the Special Issue Computational Intelligence in Remote Sensing)
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Open AccessArticle
Dimension Reduction for Hyperspectral Remote Sensor Data Based on Multi-Objective Particle Swarm Optimization Algorithm and Game Theory
Sensors 2019, 19(6), 1327; https://doi.org/10.3390/s19061327 - 16 Mar 2019
Cited by 3
Abstract
Information entropy and interclass separability are adopted as the evaluation criteria of dimension reduction for hyperspectral remote sensor data. However, it is rather single-faceted to simply use either information entropy or interclass separability as evaluation criteria, and will lead to a single-target problem. [...] Read more.
Information entropy and interclass separability are adopted as the evaluation criteria of dimension reduction for hyperspectral remote sensor data. However, it is rather single-faceted to simply use either information entropy or interclass separability as evaluation criteria, and will lead to a single-target problem. In this case, the chosen optimal band combination may be unfavorable for the improvement of follow-up classification accuracy. Thus, in this work, inter-band correlation is considered as the premise, and information entropy and interclass separability are synthesized as the evaluation criterion of dimension reduction. The multi-objective particle swarm optimization algorithm is easy to implement and characterized by rapid convergence. It is adopted to search for the optimal band combination. In addition, game theory is also introduced to dimension reduction to coordinate potential conflicts when both information entropy and interclass separability are used to search for the optimal band combination. Experimental results reveal that compared with the dimensionality reduction method, which only uses information entropy or Bhattacharyya distance as the evaluation criterion, and the method combining multiple criterions into one by weighting, the proposed method achieves global optimum more easily, and then obtains a better band combination and possess higher classification accuracy. Full article
(This article belongs to the Special Issue Computational Intelligence in Remote Sensing)
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Open AccessArticle
Multi-GPU Based Parallel Design of the Ant Colony Optimization Algorithm for Endmember Extraction from Hyperspectral Images
Sensors 2019, 19(3), 598; https://doi.org/10.3390/s19030598 - 31 Jan 2019
Cited by 1
Abstract
Spectral unmixing is a vital procedure in hyperspectral remote sensing image exploitation. The linear mixture model has been widely utilized to unmix hyperspectral images by extracting a set of pure spectral signatures, called endmembers in hyperspectral jargon, and estimating their respective fractional abundances [...] Read more.
Spectral unmixing is a vital procedure in hyperspectral remote sensing image exploitation. The linear mixture model has been widely utilized to unmix hyperspectral images by extracting a set of pure spectral signatures, called endmembers in hyperspectral jargon, and estimating their respective fractional abundances in each pixel of the scene. Many algorithms have been proposed to extract endmembers automatically, which is a critical step in the spectral unmixing chain. In recent years, the ant colony optimization (ACO) algorithm has been developed for endmember extraction from hyperspectral data, which was regarded as a combinatorial optimization problem. Although the ACO for endmember extraction (ACOEE) can acquire accurate endmember results, its high computational complexity has limited its application in the hyperspectral data analysis. The GPUs parallel computing technique can be utilized to improve the computational performance of ACOEE, but the architecture of GPUs determines that the ACOEE should be redesigned to take full advantage of computing resources on GPUs. In this paper, a multiple sub-ant-colony-based parallel design of ACOEE was proposed, in which an innovative mechanism of local pheromone for sub-ant-colonies is utilized to enable ACOEE to be preferably executed on the multi-GPU system. The proposed method can avoid much synchronization among different GPUs to affect the computational performance improvement. The experiments on two real hyperspectral datasets demonstrated that the computational performance of ACOEE significantly benefited from the proposed methods. Full article
(This article belongs to the Special Issue Computational Intelligence in Remote Sensing)
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Open AccessArticle
Estimating Spatial and Temporal Trends in Environmental Indices Based on Satellite Data: A Two-Step Approach
Sensors 2019, 19(2), 361; https://doi.org/10.3390/s19020361 - 17 Jan 2019
Cited by 2
Abstract
This paper presents a method for employing satellite data to evaluate spatial and temporal patterns in environmental indices of interest. In the first step, linear regression coefficients are extracted for each area in the image. These coefficients are then employed as a response [...] Read more.
This paper presents a method for employing satellite data to evaluate spatial and temporal patterns in environmental indices of interest. In the first step, linear regression coefficients are extracted for each area in the image. These coefficients are then employed as a response variable in a boosted regression tree with geographic coordinates as explanatory variables. Here, a two-step approach is described in the context of a substantive case study comprising 30 years of satellite derived fractional green vegetation cover for a large region in Queensland, Australia. In addition to analysis of the entire image and timeframe, separate analyses are undertaken over decades and over sub-regions of the study region. The results demonstrate both the utility of the approach and insights into spatio-temporal trends in green vegetation for this site. These findings support the feasibility of using the proposed two-step approach and geographic coordinates in the analysis of satellite derived indices over space and time. Full article
(This article belongs to the Special Issue Computational Intelligence in Remote Sensing)
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Open AccessArticle
A Comparative Study on Evolutionary Multi-objective Optimization Algorithms Estimating Surface Duct
Sensors 2018, 18(12), 4428; https://doi.org/10.3390/s18124428 - 14 Dec 2018
Cited by 4
Abstract
The problem of atmospheric duct inversion is usually solved as a single objective optimization problem. Based on ground-based Global Positioning System (GPS) phase delay and propagation loss, this paper develops a multi-objective method including the effect of source frequency and receiving antenna height. [...] Read more.
The problem of atmospheric duct inversion is usually solved as a single objective optimization problem. Based on ground-based Global Positioning System (GPS) phase delay and propagation loss, this paper develops a multi-objective method including the effect of source frequency and receiving antenna height. The diversity and convergence of solution sets are evaluated for seven multi-objective evolutionary algorithms with three performance metrics: Hypervolume (HV), Inverted Generational Distance (IGD), and the averaged Hausdorff distance ( Δ 2 ). The inversion results are compared with the simulation results, and the experimental comparison is conducted on three groups of test situations. The results demonstrate that the ranking of algorithm performance varies because of the different methods used to calculate performance metrics. Moreover, when the algorithms show overwhelming performance using performance metrics, the inversion result is not more close to the real value. In the comparison of computational experiments, it was found that, as the retrieved parameter dimension increases, the inversion result becomes more unstable. When the observed data are sufficient, the inversion result seems to be improved. Full article
(This article belongs to the Special Issue Computational Intelligence in Remote Sensing)
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