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Special Issue "Selected Papers from the “International Symposium on Remote Sensing 2018”"

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

Deadline for manuscript submissions: 31 October 2018

Special Issue Editors

Guest Editor
Prof. Hyung-Sup Jung

Department of Geoinformatics, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02120, Republic of Korea
Website | E-Mail
Phone: +82-2-6490-2892
Interests: Remote Sensing; Geoinformatics; Satellite Data Processing; Environmental Sensing
Guest Editor
Dr. Joo-Hyung Ryu

Korea Ocean Satellite Center, Korea Institute of Ocean Science & Technology(KIOST), 385 Haeyang-ro, Yeongdo-gu, Busan Metropolitan City, 49111, Republic of Korea
E-Mail
Interests: Coastal Application; Marine Surveillance System; Ocean Color
Guest Editor
Prof. Sang-Eun Park

Department of Geoinformation Engineering, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 143-747, Republic of Korea
Website | E-Mail
Interests: Radar Polarimetry; forward and inverse modeling of microwave scattering; data fusion
Guest Editor
Prof. Hoonyol Lee

Division of Geology and Geophysics, Kangwon National University, Hyoja-dong, Chuncheon, Kangwon-do 24341, Republic of Korea
Website | E-Mail
Interests: SAR Interferometry; Cryosphere; Geophysical Inversion
Guest Editor
Prof. No-Wook Park

Department of Geoinformatic Engineering, Inha University, Incheon 22212, Republic of Korea
Website | E-Mail
Interests: Remote Sensing Data Classification; Geostatistics; Machine Learning; Environmental Modeling

Special Issue Information

Dear Colleagues,

The International Symposium on Remote Sensing 2018 (ISRS 2018) is scheduled to be held in Pyeongchang, Korea, 9–11 May 2018 (http://isrs.or.kr/). This is the premier symposium that provides all participants with invaluable opportunities for catching up on state-of-the art techniques and the latest developments in remote sensing, but also serves for sharing new ideas and information with colleagues and young scholars engaged in similar studies, research or activity. This Special Issue in Remote Sensing is planned in conjunction with ISRS 2018 and will include peer-reviewed feature papers presented at ISRS 2018. Remote Sensing is an open access journal about the science and applications of remote sensing technology, and is published online. The ISRS 2018 conference papers must be fleshed out with, not only a more detailed presentation of the research, but also additional data sets and comparisons in an enhanced experimental section so that it can be published in Remote Sensing.

In the cover letter, authors should provide the corresponding paper number of ISRS 2018. If this information is not provided, the paper will not be considered as a Special Issue paper.

Prof. Hyung-Sup Jung
Dr. Joo-Hyung Ryu
Prof. Sang-Eun Park
Prof. Hoonyol Lee
Prof. No-Wook Park
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. Remote Sensing is an international peer-reviewed open access monthly 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 1800 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

  • International Symposium on Remote Sensing 2018;
  • Remote Sensing;
  • Geoinformatics;
  • Geoscience Information System (GIS);
  • Global Positioning System (GPS);
  • Image Processing

Published Papers (2 papers)

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Research

Open AccessArticle A Performance Evaluation of a Geo-Spatial Image Processing Service Based on Open Source PaaS Cloud Computing Using Cloud Foundry on OpenStack
Remote Sens. 2018, 10(8), 1274; https://doi.org/10.3390/rs10081274
Received: 13 June 2018 / Revised: 11 August 2018 / Accepted: 13 August 2018 / Published: 13 August 2018
PDF Full-text (3939 KB) | HTML Full-text | XML Full-text
Abstract
Recently, web application services based on cloud computing technologies are being offered. In the web-based application field of geo-spatial data management or processing, data processing services are produced or operated using various information communication technologies. Platform-as-a-Service (PaaS) is a type of cloud computing
[...] Read more.
Recently, web application services based on cloud computing technologies are being offered. In the web-based application field of geo-spatial data management or processing, data processing services are produced or operated using various information communication technologies. Platform-as-a-Service (PaaS) is a type of cloud computing service model that provides a platform that allows service providers to implement, execute, and manage applications without the complexity of establishing and maintaining the lower-level infrastructure components, typically related to application development and launching. There are advantages, in terms of cost-effectiveness and service development expansion, of applying non-proprietary PaaS cloud computing. Nevertheless, there have not been many studies on the use of PaaS technologies to build geo-spatial application services. This study was based on open source PaaS technologies used in a geo-spatial image processing service, and it aimed to evaluate the performance of that service in relation to the Web Processing Service (WPS) 2.0 specification, based on the Open Geospatial Consortium (OGC) after a test application deployment using the configured service supported by a cloud environment. Using these components, the performance of an edge extraction algorithm on the test system in three cases, of 300, 500, and 700 threads, was assessed through a comparison test with another test system, in the same three cases, using Infrastructure-as-a-Service (IaaS) without Load Balancer-as-a-Service (LBaaS). According to the experiment results, in all the test cases of WPS execution considered in this study, the PaaS-based geo-spatial service had a greater performance and lower error rates than the IaaS-based cloud without LBaaS. Full article
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Graphical abstract

Open AccessArticle Application of Ensemble-Based Machine Learning Models to Landslide Susceptibility Mapping
Remote Sens. 2018, 10(8), 1252; https://doi.org/10.3390/rs10081252
Received: 21 June 2018 / Revised: 3 August 2018 / Accepted: 5 August 2018 / Published: 9 August 2018
PDF Full-text (5418 KB) | HTML Full-text | XML Full-text
Abstract
The main purpose of this study was to produce landslide susceptibility maps using various ensemble-based machine learning models (i.e., the AdaBoost, LogitBoost, Multiclass Classifier, and Bagging models) for the Sacheon-myeon area of South Korea. A landslide inventory map including a total of 762
[...] Read more.
The main purpose of this study was to produce landslide susceptibility maps using various ensemble-based machine learning models (i.e., the AdaBoost, LogitBoost, Multiclass Classifier, and Bagging models) for the Sacheon-myeon area of South Korea. A landslide inventory map including a total of 762 landslides was compiled based on reports and aerial photograph interpretations. The landslides were randomly separated into two datasets: 70% of landslides were selected for the model establishment and 30% were used for validation purposes. Additionally, 20 landslide condition factors divided into five categories (topographic factors, hydrological factors, soil map, geological map, and forest map) were considered in the landslide susceptibility mapping. The relationships among landslide occurrence and landslide conditioning factors were analyzed and the landslide susceptibility maps were calculated and drawn using the AdaBoost, LogitBoost, Multiclass Classifier, and Bagging models. Finally, the maps were validated using the area under the curve (AUC) method. The Multiclass Classifier method had higher prediction accuracy (85.9%) than the Bagging (AUC = 85.4%), LogitBoost (AUC = 84.8%), and AdaBoost (84.0%) methods. Full article
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