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Remote Sensing
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Data Mining in Multi-Platform Remote Sensing
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Data Mining in Multi-Platform Remote Sensing

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (15 January 2022) | Viewed by 26814

Special Issue Editors

Dr. Bardia Yousefi

E-Mail Website
Guest Editor
1. University of Pennsylvania Goddard Bldg., 3710 Hamilton Walk, Philadelphia, PA 19104, USA
2. Computer Vision and System Laboratory (CVSL), Department of Electrical and Computer Engineering, Laval University, Quebec City, QC, Canada
Interests: infrared hyperspectral imagery; pattern recognition; machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Rubén Usamentiaga

E-Mail Website
Guest Editor
Computer Science and Engineering, University of Oviedo, 33204 Gijón, Asturias, Spain
Interests: infrared thermography; computer vision; real-time image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The importance of computational imaging techniques is unequivocally recognized in different fields of remote sensing for a variety of applications including geology, mineralogy, environmental, and target detection applications. Many techniques have been developed to strengthen previous approaches by improving the enhancement and accuracy of performance, reliability, and computational time. Such methods are considered attractive to scientists, engineers, and industries.

This Special Issue will collect new developments and methodologies, best practices, and applications of datamining methods for remote sensing. We welcome submissions that provide the community with the most recent advancements in all aspects of datamining methods in remote sensing, including but not limited to the following:

  • Data processing, machine learning, and pattern recognition approach in infrared and thermography. and photogrammetry
  • Geothermal and mineral mapping (infrared thermography, portable instruments, drill core imagery, etc.)
  • Data analysis (image classification, feature extraction, target detection, change detection, biophysical parameter estimation, etc.)
  • Platforms and new sensors onboard (multispectral, hyperspectral, thermal, lidar, SAR, gas or radioactivity sensors, etc.)
  • Data fusion: integration of UAV imagery with satellite, aerial or terrestrial data, integration of heterogeneous data captured by UAVs, real-time processing / collaborative and UAV fleets applied to remote sensing
  • Applications (geology, mineralogy, 3D mapping, urban monitoring, precision farming, forestry, disaster prevention, assessment and monitoring, search and rescue, security, archaeology, industrial plant inspection, etc.)

Dr. Bardia Yousefi
Prof. Dr. Rubén Usamentiaga
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

  • data processing
  • data fusion
  • machine learning
  • pattern recognition
  • target detection
  • hyperspectral infrared imagery

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Published Papers (5 papers)

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Research

20 pages, 2217 KiB  
Article
Marine Online Platforms of Services to Public End-Users—The Innovation of the ODYSSEA Project
by Meysam Majidi Nezhad, Mehdi Neshat, Giuseppe Piras, Davide Astiaso Garcia and Georgios Sylaios
Remote Sens. 2022, 14(3), 572; https://doi.org/10.3390/rs14030572 - 25 Jan 2022
Cited by 4 | Viewed by 3140
Abstract
Recently, various Earth Observation Networks (EONs) have been designed, developed and launched by in-situ, on-site and off-site collected data from fixed and moving marine sensors and remote sensing (RS) satellite data. This information can significantly help a wide range of public and private [...] Read more.
Recently, various Earth Observation Networks (EONs) have been designed, developed and launched by in-situ, on-site and off-site collected data from fixed and moving marine sensors and remote sensing (RS) satellite data. This information can significantly help a wide range of public and private end-users better understand the medium- and high-resolution numerical models for regional, national and global coverage. In this context, such EON core services’ operational numerical data can be seen of the growing demand result for marine sustainability development of developing countries and the European Union (EU). In this case, marine platforms can offer a wide range of benefits to users of human communities in the same environment using meticulous analyses. Furthermore, marine platforms can contribute to a deeper discourse on the ocean, given the required regulations, technical and legal considerations and users to a common typology using clear scientific terminology. In this regard, firstly, the following six steps have been used to develop a better understanding of the essential data structure that is commensurate with the efficiency of the marine end-user’s service: (1) steps and challenges of collecting data, (2) stakeholder engagement to identify, detect and assess the specific needs of end-users, (3) design, develop and launch the products offered to meet the specific needs of users, (4) achieve sustainable development in the continuous provision of these products to end-users, (5) identify future needs and challenges, and (6) online platform architecture style related to providing these products to end-users. Secondly, the innovation of the ODYSSEA (Operating a Network of Integrated Observatory Systems in the Mediterranean Sea) platform project has been evaluated and reviewed as a successful project on marine online platforms to better understand how marine online platforms are being used, designed, developed and launched. The ODYSSEA platform provides a system that bridges the gap between operational oceanographic capabilities and the need for information on marine conditions, including for the end-user community. The project aims to develop a fully integrated and cost-effective cross-platform, multi-platform network of observation and forecasting systems across the Mediterranean Sea. Full article
(This article belongs to the Special Issue Data Mining in Multi-Platform Remote Sensing)
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24 pages, 2399 KiB  
Article
Wildfire Segmentation Using Deep Vision Transformers
by Rafik Ghali, Moulay A. Akhloufi, Marwa Jmal, Wided Souidene Mseddi and Rabah Attia
Remote Sens. 2021, 13(17), 3527; https://doi.org/10.3390/rs13173527 - 5 Sep 2021
Cited by 62 | Viewed by 9656
Abstract
In this paper, we address the problem of forest fires’ early detection and segmentation in order to predict their spread and help with fire fighting. Techniques based on Convolutional Networks are the most used and have proven to be efficient at solving such [...] Read more.
In this paper, we address the problem of forest fires’ early detection and segmentation in order to predict their spread and help with fire fighting. Techniques based on Convolutional Networks are the most used and have proven to be efficient at solving such a problem. However, they remain limited in modeling the long-range relationship between objects in the image, due to the intrinsic locality of convolution operators. In order to overcome this drawback, Transformers, designed for sequence-to-sequence prediction, have emerged as alternative architectures. They have recently been used to determine the global dependencies between input and output sequences using the self-attention mechanism. In this context, we present in this work the very first study, which explores the potential of vision Transformers in the context of forest fire segmentation. Two vision-based Transformers are used, TransUNet and MedT. Thus, we design two frameworks based on the former image Transformers adapted to our complex, non-structured environment, which we evaluate using varying backbones and we optimize for forest fires’ segmentation. Extensive evaluations of both frameworks revealed a performance superior to current methods. The proposed approaches achieved a state-of-the-art performance with an F1-score of 97.7% for TransUNet architecture and 96.0% for MedT architecture. The analysis of the results showed that these models reduce fire pixels mis-classifications thanks to the extraction of both global and local features, which provide finer detection of the fire’s shape. Full article
(This article belongs to the Special Issue Data Mining in Multi-Platform Remote Sensing)
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Graphical abstract

21 pages, 19661 KiB  
Article
Cost-Performance Evaluation of a Recognition Service of Livestock Activity Using Aerial Images
by Darío G. Lema, Oscar D. Pedrayes, Rubén Usamentiaga, Daniel F. García and Ángela Alonso
Remote Sens. 2021, 13(12), 2318; https://doi.org/10.3390/rs13122318 - 13 Jun 2021
Cited by 12 | Viewed by 3350
Abstract
The recognition of livestock activity is essential to be eligible for subsides, to automatically supervise critical activities and to locate stray animals. In recent decades, research has been carried out into animal detection, but this paper also analyzes the detection of other key [...] Read more.
The recognition of livestock activity is essential to be eligible for subsides, to automatically supervise critical activities and to locate stray animals. In recent decades, research has been carried out into animal detection, but this paper also analyzes the detection of other key elements that can be used to verify the presence of livestock activity in a given terrain: manure piles, feeders, silage balls, silage storage areas, and slurry pits. In recent years, the trend is to apply Convolutional Neuronal Networks (CNN) as they offer significantly better results than those obtained by traditional techniques. To implement a livestock activity detection service, the following object detection algorithms have been evaluated: YOLOv2, YOLOv4, YOLOv5, SSD, and Azure Custom Vision. Since YOLOv5 offers the best results, producing a mean average precision (mAP) of 0.94, this detector is selected for the creation of a livestock activity recognition service. In order to deploy the service in the best infrastructure, the performance/cost ratio of various Azure cloud infrastructures are analyzed and compared with a local solution. The result is an efficient and accurate service that can help to identify the presence of livestock activity in a specified terrain. Full article
(This article belongs to the Special Issue Data Mining in Multi-Platform Remote Sensing)
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33 pages, 19018 KiB  
Article
Evaluation of Semantic Segmentation Methods for Land Use with Spectral Imaging Using Sentinel-2 and PNOA Imagery
by Oscar D. Pedrayes, Darío G. Lema, Daniel F. García, Rubén Usamentiaga and Ángela Alonso
Remote Sens. 2021, 13(12), 2292; https://doi.org/10.3390/rs13122292 - 11 Jun 2021
Cited by 19 | Viewed by 4548
Abstract
Land use classification using aerial imagery can be complex. Characteristics such as ground sampling distance, resolution, number of bands and the information these bands convey are the keys to its accuracy. Random Forest is the most widely used approach but better and more [...] Read more.
Land use classification using aerial imagery can be complex. Characteristics such as ground sampling distance, resolution, number of bands and the information these bands convey are the keys to its accuracy. Random Forest is the most widely used approach but better and more modern alternatives do exist. In this paper, state-of-the-art methods are evaluated, consisting of semantic segmentation networks such as UNet and DeepLabV3+. In addition, two datasets based on aircraft and satellite imagery are generated as a new state of the art to test land use classification. These datasets, called UOPNOA and UOS2, are publicly available. In this work, the performance of these networks and the two datasets generated are evaluated. This paper demonstrates that ground sampling distance is the most important factor in obtaining good semantic segmentation results, but a suitable number of bands can be as important. This proves that both aircraft and satellite imagery can produce good results, although for different reasons. Finally, cost performance for an inference prototype is evaluated, comparing various Microsoft Azure architectures. The evaluation concludes that using a GPU is unnecessarily costly for deployment. A GPU need only be used for training. Full article
(This article belongs to the Special Issue Data Mining in Multi-Platform Remote Sensing)
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Graphical abstract

25 pages, 5679 KiB  
Article
Unsupervised Identification of Targeted Spectra Applying Rank1-NMF and FCC Algorithms in Long-Wave Hyperspectral Infrared Imagery
by Bardia Yousefi, Clemente Ibarra-Castanedo, Martin Chamberland, Xavier P. V. Maldague and Georges Beaudoin
Remote Sens. 2021, 13(11), 2125; https://doi.org/10.3390/rs13112125 - 28 May 2021
Cited by 3 | Viewed by 4415
Abstract
Clustering methods unequivocally show considerable influence on many recent algorithms and play an important role in hyperspectral data analysis. Here, we challenge the clustering for mineral identification using two different strategies in hyperspectral long wave infrared (LWIR, 7.7–11.8 μm). For that, we compare [...] Read more.
Clustering methods unequivocally show considerable influence on many recent algorithms and play an important role in hyperspectral data analysis. Here, we challenge the clustering for mineral identification using two different strategies in hyperspectral long wave infrared (LWIR, 7.7–11.8 μm). For that, we compare two algorithms to perform the mineral identification in a unique dataset. The first algorithm uses spectral comparison techniques for all the pixel-spectra and creates RGB false color composites (FCC). Then, a color based clustering is used to group the regions (called FCC-clustering). The second algorithm clusters all the pixel-spectra to directly group the spectra. Then, the first rank of non-negative matrix factorization (NMF) extracts the representative of each cluster and compares results with the spectral library of JPL/NASA. These techniques give the comparison values as features which convert into RGB-FCC as the results (called clustering rank1-NMF). We applied K-means as clustering approach, which can be modified in any other similar clustering approach. The results of the clustering-rank1-NMF algorithm indicate significant computational efficiency (more than 20 times faster than the previous approach) and promising performance for mineral identification having up to 75.8% and 84.8% average accuracies for FCC-clustering and clustering-rank1 NMF algorithms (using spectral angle mapper (SAM)), respectively. Furthermore, several spectral comparison techniques are used also such as adaptive matched subspace detector (AMSD), orthogonal subspace projection (OSP) algorithm, principal component analysis (PCA), local matched filter (PLMF), SAM, and normalized cross correlation (NCC) for both algorithms and most of them show a similar range in accuracy. However, SAM and NCC are preferred due to their computational simplicity. Our algorithms strive to identify eleven different mineral grains (biotite, diopside, epidote, goethite, kyanite, scheelite, smithsonite, tourmaline, pyrope, olivine, and quartz). Full article
(This article belongs to the Special Issue Data Mining in Multi-Platform Remote Sensing)
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