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Remote Sensing
Special Issues
Machine Learning Techniques for Remote Sensing and Electromagnetic Applications
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Machine Learning Techniques for Remote Sensing and Electromagnetic Applications

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

Deadline for manuscript submissions: closed (31 January 2020) | Viewed by 16772

Special Issue Editors

Prof. Dr. David J. Lary

E-Mail Website
Guest Editor
Hanson Center for Space Sciences, University of Texas at Dallas, 800 W. Campbell Rd, Richardson, TX 75080, USA
Interests: service of society using machine learning; remote sensing; smart cities; IOT; remote control vehicles (aerial, water and ground); data driven scientific discovery; data driven insights and decision support
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Rita Asquini

E-Mail Website
Guest Editor
Department of Information Engineering, Electronics and Telecommunications, University of Rome "La Sapienza", Via Eudossiana 18, 00184 Rome, Italy
Interests: electronic engineering; optoelectronics; photonics; solar cells; optical quantum systems; neural networks
Prof. Dr. Massimo Panella

E-Mail Website
Chief Guest Editor
Department of Information Engineering, Electronics and Telecommunications (DIET), Sapienza University of Rome, Via Eudossiana 18, 00184 Rome, Italy
Interests: deep learning; computational intelligence; smart sensor networks; quantum computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims at fostering scientific exchanges and new enhancements among researchers working in the field of remote sensing, and on electromagnetic problems as well, where the use of machine learning techniques can lead to innovative solutions and/or to more efficient applications. Contributions engaging theoretical foundations based on computational intelligence (i.e., neural networks, fuzzy logic, evolutionary computation, deep learning, etc.) or those using such computational techniques in practical applications (i.e., pattern recognition, data regression and classification, time series prediction, inverse modeling, multi-spectral image and data processing, sensor networks, and so forth) are advised to submit a paper to this Special Issue.

Prof. Massimo Panella
Prof. David J. Lary
Prof. Rita Asquini
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

  • machine learning
  • neural and fuzzy neural networks
  • evolutionary computation
  • deep learning
  • pattern recognition and data mining
  • data regression and classification
  • inverse modeling
  • multi-spectral image and data processing

Published Papers (1 paper)

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Research

16 pages, 62213 KiB  
Article
Applying Deep Learning to Automate UAV-Based Detection of Scatterable Landmines
by Jasper Baur, Gabriel Steinberg, Alex Nikulin, Kenneth Chiu and Timothy S. de Smet
Remote Sens. 2020, 12(5), 859; https://doi.org/10.3390/rs12050859 - 6 Mar 2020
Cited by 27 | Viewed by 16050
Abstract
Recent advances in unmanned-aerial-vehicle- (UAV-) based remote sensing utilizing lightweight multispectral and thermal infrared sensors allow for rapid wide-area landmine contamination detection and mapping surveys. We present results of a study focused on developing and testing an automated technique of remote landmine detection [...] Read more.
Recent advances in unmanned-aerial-vehicle- (UAV-) based remote sensing utilizing lightweight multispectral and thermal infrared sensors allow for rapid wide-area landmine contamination detection and mapping surveys. We present results of a study focused on developing and testing an automated technique of remote landmine detection and identification of scatterable antipersonnel landmines in wide-area surveys. Our methodology is calibrated for the detection of scatterable plastic landmines which utilize a liquid explosive encapsulated in a polyethylene or plastic body in their design. We base our findings on analysis of multispectral and thermal datasets collected by an automated UAV-survey system featuring scattered PFM-1-type landmines as test objects and present results of an effort to automate landmine detection, relying on supervised learning algorithms using a Faster Regional-Convolutional Neural Network (Faster R-CNN). The RGB visible light Faster R-CNN demo yielded a 99.3% testing accuracy for a partially withheld testing set and 71.5% testing accuracy for a completely withheld testing set. Across multiple test environments, using centimeter scale accurate georeferenced datasets paired with Faster R-CNN, allowed for accurate automated detection of test PFM-1 landmines. This method can be calibrated to other types of scatterable antipersonnel mines in future trials to aid humanitarian demining initiatives. With millions of remnant PFM-1 and similar scatterable plastic mines across post-conflict regions and considerable stockpiles of these landmines posing long-term humanitarian and economic threats to impacted communities, our methodology could considerably aid in efforts to demine impacted regions. Full article
(This article belongs to the Special Issue Machine Learning Techniques for Remote Sensing and Electromagnetic Applications)
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