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Recent Advances in Space & Sensor Technologies and Remote Sensing...
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Recent Advances in Space & Sensor Technologies and Remote Sensing Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microwave and Wireless Communications".

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 9444

Special Issue Editors

Prof. Dr. Toshifumi Moriyama

E-Mail Website
Guest Editor
Graduate School of Engineering, Nagasaki University, 1-14 Bunkyo-machi, Nagasaki 852-8521, Japan
Interests: radar polarimetry; inverse scattering; microwave remote-sensing; wireless sensor networks
Special Issues, Collections and Topics in MDPI journals
Dr. Chansu Yang

E-Mail Website
Guest Editor
Korea Institute of Ocean Science and Technology(KIOST), Maritime Safety Research Center, Busan, Korea
Interests: SAR applications; maritime safety and security; data fusion
Prof. Dr. Hirokazu Kobayashi

E-Mail Website
Guest Editor
Department of Electronics and Information Systems Engineering, Osaka Institute of Technology, Osaka 535-8585, Japan
Interests: radar imaging; inverse synthetic aperture radar; electromagnetic modeling; radar cross-section theory and measurement; radar beam scanning; radar signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The research and development work in the field of Earth observation began with Landsat-1. Since then, various Earth observation satellites have been launched and operated. As a result, application and utilization in various fields of practical use and science such as geology, ocean, agriculture, resource exploration, and fishery have spread rapidly. In recent years, the development of radar technology including synthetic aperture radar has been remarkable, and related technologies such as interferometry, differential interferometry, and polarimetry have also been developed. Deep learning and machine learning are applied to Earth observation data analysis. Furthermore, in order to handle spatial information including Earth observation data, GIS/GPS technology has become very important and has attracted great interest. Many international conferences, including ICSANE (International Conference on Space, Aeronautical and Navigational Electronics), have provided a forum for research and development in these fields. Therefore, this Special Issue focuses on the contents of recent Space and Sensor Technologies and Remote Sensing Applications. Topics of interest include but are not limited to the following:

(1) Satellite and space-station systems;
(2) Remote sensing and scientific observation technology;
(3) Radar systems and applications;
(4) Navigational and communication systems.

The authors of the papers which will be presented at International Conference on Space, Aeronautical and Navigation Electronics 2019, being organized at Jeju National University, Jeju, Korea, on 31 October–1 November 2019 are invited to submit their related article to this Special Issue of the journal Electronics. There are no page limitations for this journal, and the contents of submitted manuscripts should not have been published previously and should be significantly independent of the ICSANE paper.

Dr. Toshifumi Moriyama
Dr. Chan-Su Yang
Prof. Dr. Hirokazu Kobayashi
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. Electronics 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 2400 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
  • radar
  • synthetic aperture radar
  • interferometry
  • polarimetry
  • GPS
  • GIS

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Published Papers (3 papers)

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Research

14 pages, 3005 KiB  
Article
Enhancement of Ship Type Classification from a Combination of CNN and KNN
by Ho-Kun Jeon and Chan-Su Yang
Electronics 2021, 10(10), 1169; https://doi.org/10.3390/electronics10101169 - 13 May 2021
Cited by 17 | Viewed by 2680
Abstract
Ship type classification of synthetic aperture radar imagery with convolution neural network (CNN) has been faced with insufficient labeled datasets, unoptimized and noised polarization images that can deteriorate a classification performance. Meanwhile, numerous labeled text information for ships, such as length and breadth, [...] Read more.
Ship type classification of synthetic aperture radar imagery with convolution neural network (CNN) has been faced with insufficient labeled datasets, unoptimized and noised polarization images that can deteriorate a classification performance. Meanwhile, numerous labeled text information for ships, such as length and breadth, can be easily obtained from various sources and can be utilized in a classification with k-nearest neighbor (KNN). This study proposes a method to improve the efficiency of ship type classification from Sentinel-1 dual-polarization data with 10 m pixel spacing using both CNN and KNN models. In the first stage, Sentinel-1 intensity images centered on ship positions were used in a rectangular shape to apply an image processing procedure such as head-up, padding and image augmentation. The process increased the accuracy by 33.0% and 31.7% for VH (vertical transmit and horizontal receive) and VV (vertical transmit and vertical receive) polarization compared to the CNN-based classification with original ship images, respectively. In the second step, a combined method of CNN and KNN was compared with a CNN-alone case. The f1-score of CNN alone was up to 85.0%, whereas the combination method showed up to 94.3%, which was a 9.3% increase. In the future, more details on an optimization method will be investigated through field experiments of ship classification. Full article
(This article belongs to the Special Issue Recent Advances in Space & Sensor Technologies and Remote Sensing Applications)
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13 pages, 3825 KiB  
Article
Spectral Analysis of Stationary Signals Based on Two Simplified Arrangements of Chirp Transform Spectrometer
by Quan Zhao, Ling Tong and Bo Gao
Electronics 2021, 10(1), 65; https://doi.org/10.3390/electronics10010065 - 31 Dec 2020
Cited by 2 | Viewed by 1955
Abstract
The classical two-channel push-pull chirp transform spectrometer (CTS) has been widely applied in satellite-borne remote sensing systems for earth observation and deep space exploration. In this paper, we present two simplified structures with single M(l)-C(s) CTS arrangements for the spectral analysis of stationary [...] Read more.
The classical two-channel push-pull chirp transform spectrometer (CTS) has been widely applied in satellite-borne remote sensing systems for earth observation and deep space exploration. In this paper, we present two simplified structures with single M(l)-C(s) CTS arrangements for the spectral analysis of stationary signals. A simplified CTS system with a single M(l)-C(s) arrangement and a time delay line was firstly developed. Another simplified structure of CTS with a M(l)-C(s) arrangement and a frequency conversion channel was also developed for spectral analysis of stationary signals. Simulation and experiment results demonstrate that the two simplified arrangements can both realize spectrum measurement for the stationary signals and obtain the same frequency resolution, amplitude accuracy and system sensitivity as that of the classical two-channel push–pull CTS system. Compared to the classical CTS structure, the two simplified arrangements require fewer devices, save power consumption and have reduced mass. The matching problem between the two channels can be avoided in the two simplified arrangements. The simplified CTS arrangements may have potential application in the spectrum measurement of stationary signals in the field of aviation and spaceflight. Full article
(This article belongs to the Special Issue Recent Advances in Space & Sensor Technologies and Remote Sensing Applications)
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9 pages, 3596 KiB  
Article
Sea Fog Identification from GOCI Images Using CNN Transfer Learning Models
by Ho-Kun Jeon, Seungryong Kim, Jonathan Edwin and Chan-Su Yang
Electronics 2020, 9(2), 311; https://doi.org/10.3390/electronics9020311 - 11 Feb 2020
Cited by 34 | Viewed by 3875
Abstract
This study proposes an approaching method of identifying sea fog by using Geostationary Ocean Color Imager (GOCI) data through applying a Convolution Neural Network Transfer Learning (CNN-TL) model. In this study, VGG19 and ResNet50, pre-trained CNN models, are used for their high identification [...] Read more.
This study proposes an approaching method of identifying sea fog by using Geostationary Ocean Color Imager (GOCI) data through applying a Convolution Neural Network Transfer Learning (CNN-TL) model. In this study, VGG19 and ResNet50, pre-trained CNN models, are used for their high identification performance. The training and testing datasets were extracted from GOCI images for the area of coastal regions of the Korean Peninsula for six days in March 2015. With varying band combinations and changing whether Transfer Learning (TL) is applied, identification experiments were executed. TL enhanced the performance of the two models. Training data of CNN-TL showed up to 96.3% accuracy in matching, both with VGG19 and ResNet50, identically. Thus, it is revealed that CNN-TL is effective for the detection of sea fog from GOCI imagery. Full article
(This article belongs to the Special Issue Recent Advances in Space & Sensor Technologies and Remote Sensing Applications)
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