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Remote Sensing
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Earth Observation from KOMPSAT Optical, Thermal and Radar Satellite Images
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Earth Observation from KOMPSAT Optical, Thermal and Radar Satellite Images

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (30 September 2020) | Viewed by 24102

Special Issue Editors

Dr. Kwang-Jae Lee

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Guest Editor
Korea Aerospace Research Institute, 169-84 Gwahak-ro, Yuseong-gu, Daejeon 34133, Korea
Interests: satellite-based remote sensing; change monitoring; time series analysis
Dr. Tae-Byeong Chae

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Guest Editor
Korea Aerospace Research Institute, 169-84 Gwahak-ro, Yuseong-gu, Daejeon 34133, Korea
Interests: target detection; deep learning; analytics
Prof. Dr. Hyung-Sup Jung

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Guest Editor
1. Department of Smart Cities, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02120, Republic of Korea
2. Department of Geoinformatics, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02120, Republic of Korea
Interests: SAR; InSAR; GeoAI; sensor modeling; satellite image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the past several decades, as sensor technology has improved, the spatial resolution of satellite images has been steadily improving. The design, performance, and applications of satellite-based remote sensors have become essential to understanding the physical, ecological, geological, hydrological, and environmental characteristics of the Earth surfaces. The KOMPSAT (Korea multipurpose satellite) program was initiated in 1995 as a major space investment in Korea, and the KOMPSAT-1, 2, 3, 3A, and 5 satellites were launched with high-resolution optical, infrared, and radar sensors. The main objective of the KOMPSAT program is to meet the international and national demands to acquire high-resolution satellite imagery. The KOMPSAT images have been successfully used to observe the Earth surface in the fields of physics, ecology, geology, hydrology, environmentalogy, etc. In this Special Issue, we invite original research articles addressing the state-of-the-art remote sensing technologies and methods using the KOMPSAT images. The objectives of this Special Issue are to discuss on recent advances in remote sensing technologies as well as new remote sensing applications using the KOMPSAT images. Manuscript submissions are encouraged to share the latest progress and achievements for advanced utilization of KOMPSAT imagery. Both experimental and theoretical/simulated results will be welcomed to this Special Issue. Potential topics include but are not limited to:

  • KOMPSAT high resolution optic, SAR, MIR (middle-wave infra-red) image processing;
  • KOMPSAT image restoration and image quality improvement;
  • Geospatial data models of KOMPSAT images;
  • Application and innovative use of KOMPSAT sensor techniques;
  • Multisensor integration and applications of KOMPSAT images;
  • KOMPSAT Image processing algorithm and systems;
  • Geospatial information extraction and data mining of KOMPSAT images;
  • Real-time emergency support systems and applications;
  • Change detection, target detection, and target recognition of KOMPSAT images;
  • KOMPSAT sensor modeling and mapping;
  • KOMPSAT image classification and segmentation;
  • AI, deep learning, and big data analysis of KOMPSAT images;
  • Time series analysis using KOMPSAT images.

Dr. Kwang-Jae Lee
Dr. Tae-Byeong Chae
Prof. Dr. Hyung-Sup Jung
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

  • KOMPSAT
  • SAR
  • MIR (middle infrared)
  • High resolution
  • Image processing
  • Deep learning
  • Change detection
  • Spatial analysis
  • Mapping
  • Classification

Published Papers (7 papers)

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Editorial

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4 pages, 3959 KiB  
Editorial
Earth Observation from KOMPSAT Optical, Thermal, and Radar Satellite Images
by Kwang-Jae Lee, Tae-Byeong Chae and Hyung-Sup Jung
Remote Sens. 2021, 13(1), 139; https://doi.org/10.3390/rs13010139 - 04 Jan 2021
Cited by 2 | Viewed by 2593
Abstract
Over the past several decades, as sensor technology has improved, the spatial resolution of satellite images has been steadily improving [...] Full article
(This article belongs to the Special Issue Earth Observation from KOMPSAT Optical, Thermal and Radar Satellite Images)

Research

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14 pages, 6530 KiB  
Article
Spatial Sharpening of KOMPSAT-3A MIR Images Using Optimal Scaling Factor
by Kwan-Young Oh, Hyung-Sup Jung, Sung-Hwan Park and Kwang-Jae Lee
Remote Sens. 2020, 12(22), 3772; https://doi.org/10.3390/rs12223772 - 17 Nov 2020
Cited by 3 | Viewed by 1955
Abstract
This paper present efficient methods for merging KOMPSAT-3A (Korea Multi-Purpose Satellite) medium wave Infrared (MIR) and panchromatic (PAN) images. Spatial sharpening techniques have been developed to create an image with both high spatial and high spectral resolution by combining the desired qualities of [...] Read more.
This paper present efficient methods for merging KOMPSAT-3A (Korea Multi-Purpose Satellite) medium wave Infrared (MIR) and panchromatic (PAN) images. Spatial sharpening techniques have been developed to create an image with both high spatial and high spectral resolution by combining the desired qualities of a PAN image with high spatial and low spectral resolution and an MS/MIR image with low spatial and high spectral resolution. The proposed methods can extract an optimal scaling factor, and uses the tactics of appropriately controlling the balance between the spatial and spectral resolutions. KOMPSAT-3A PAN and MIR images were used to test and evaluate the performance of the proposed methods. A qualitative assessment were performed using the image quality index (Q4), the cross correlation index (CC) and the relative global dimensional synthesis error (Spectral/Spatial ERGAS). These tests indicate that the proposed methods preserve the spectral and spatial characteristics of the original MIR and PAN images. Visual analysis reveals that the spectral and spatial information derived from the proposed methods were well retained in the test images. A comparison of the results of the proposed methods with those obtained from applying existing ones such as the Multi Sensor Fusion (MSF) technique or the Guide Filter Based Fusion (GF) show the efficiency of the new fusion process to be superior to the one of the others. The results showed a significant improvement in fusion capability for KOMPSAT-3A MIR imagery. Full article
(This article belongs to the Special Issue Earth Observation from KOMPSAT Optical, Thermal and Radar Satellite Images)
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17 pages, 4131 KiB  
Article
Classification of Landscape Affected by Deforestation Using High-Resolution Remote Sensing Data and Deep-Learning Techniques
by Seong-Hyeok Lee, Kuk-Jin Han, Kwon Lee, Kwang-Jae Lee, Kwan-Young Oh and Moung-Jin Lee
Remote Sens. 2020, 12(20), 3372; https://doi.org/10.3390/rs12203372 - 15 Oct 2020
Cited by 45 | Viewed by 5456
Abstract
Human-induced deforestation has a major impact on forest ecosystems and therefore its detection and analysis methods should be improved. This study classified landscape affected by human-induced deforestation efficiently using high-resolution remote sensing and deep-learning. The SegNet and U-Net algorithms were selected for application [...] Read more.
Human-induced deforestation has a major impact on forest ecosystems and therefore its detection and analysis methods should be improved. This study classified landscape affected by human-induced deforestation efficiently using high-resolution remote sensing and deep-learning. The SegNet and U-Net algorithms were selected for application with high-resolution remote sensing data obtained by the Kompsat-3 satellite. Land and forest cover maps were used as base data to construct accurate deep-learning datasets of deforested areas at high spatial resolution, and digital maps and a softwood database were used as reference data. Sites were classified into forest and non-forest areas, and a total of 13 areas (2 forest and 11 non-forest) were selected for analysis. Overall, U-Net was more accurate than SegNet (74.8% vs. 63.3%). The U-Net algorithm was about 11.5% more accurate than the SegNet algorithm, although SegNet performed better for the hardwood and bare land classes. The SegNet algorithm misclassified many forest areas, but no non-forest area. There was reduced accuracy of the U-Net algorithm due to misclassification among sub-items, but U-Net performed very well at the forest/non-forest area classification level, with 98.4% accuracy for forest areas and 88.5% for non-forest areas. Thus, deep-learning modeling has great potential for estimating human-induced deforestation in mountain areas. The findings of this study will contribute to more efficient monitoring of damaged mountain forests and the determination of policy priorities for mountain area restoration. Full article
(This article belongs to the Special Issue Earth Observation from KOMPSAT Optical, Thermal and Radar Satellite Images)
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22 pages, 3476 KiB  
Article
Acceleration Compensation for Estimation of Along-Track Velocity of Ground Moving Target from Single-Channel SAR SLC Data
by Sang-Wan Kim and Joong-Sun Won
Remote Sens. 2020, 12(10), 1609; https://doi.org/10.3390/rs12101609 - 18 May 2020
Cited by 7 | Viewed by 2665
Abstract
Across-track acceleration is a major source of estimation error of along-track velocity in synthetic-aperture radar (SAR) ground moving-target indication (GMTI). This paper presents the theory and a method of compensating across-track acceleration to improve the accuracy of along-track velocity estimated from single-channel SAR [...] Read more.
Across-track acceleration is a major source of estimation error of along-track velocity in synthetic-aperture radar (SAR) ground moving-target indication (GMTI). This paper presents the theory and a method of compensating across-track acceleration to improve the accuracy of along-track velocity estimated from single-channel SAR single-look complex data. A unique feature of the proposed method is the utilisation of phase derivatives in the Doppler frequency domain, which is effective for azimuth-compressed signals. The performance of the method was evaluated through experimental data acquired by TerraSAR-X and speed-controlled and measured vehicles. The application results demonstrate a notable improvement in along-track velocity estimates. The amount of along-track velocity correction is particularly significant when a target has irregular motion with a low signal-to-clutter ratio. A discontinuous velocity jump rather than a constant acceleration was also observed and verified through comparison between actual data and simulations. By applying this method, the capability of single-channel SAR GMTI could be substantially improved in terms of accuracy of velocity, and moving direction. However, the method is effective only if the correlation between the actual Doppler phase derivatives and a model derived from the residual Doppler rate is sufficiently high. The proposed method will be applied to X-band SAR systems of KOMPSAT-5 and -6. Full article
(This article belongs to the Special Issue Earth Observation from KOMPSAT Optical, Thermal and Radar Satellite Images)
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17 pages, 7914 KiB  
Article
Mapping Forest Vertical Structure in Jeju Island from Optical and Radar Satellite Images Using Artificial Neural Network
by Yong-Suk Lee, Sunmin Lee, Won-Kyung Baek, Hyung-Sup Jung, Sung-Hwan Park and Moung-Jin Lee
Remote Sens. 2020, 12(5), 797; https://doi.org/10.3390/rs12050797 - 02 Mar 2020
Cited by 17 | Viewed by 3969
Abstract
Recently, due to the acceleration of global warming, an accurate understanding and management of forest carbon stocks, such as forest aboveground biomass, has become very important. The vertical structure of the forest, which is the internal structure of the forest, was mainly investigated [...] Read more.
Recently, due to the acceleration of global warming, an accurate understanding and management of forest carbon stocks, such as forest aboveground biomass, has become very important. The vertical structure of the forest, which is the internal structure of the forest, was mainly investigated by field surveys that are labor intensive. Recently, remote sensing techniques have been actively used to explore large and inaccessible areas. In addition, machine learning techniques that could classify and analyze large amounts of data are being used in various fields. Thus, this study aims to analyze the forest vertical structure (number of tree layers) to estimate forest aboveground biomass in Jeju Island from optical and radar satellite images using artificial neural networks (ANN). For this purpose, the eight input neurons of the forest related layers, based on remote sensing data, were prepared: normalized difference vegetation index (NDVI), normalized difference water index (NDWI), NDVI texture, NDWI texture, average canopy height, standard deviation canopy height and two types of coherence maps were created using the Kompsat-3 optical image, L-band ALOS PALSAR-1 radar images, digital surface model (DSM), and digital terrain model (DTM). The forest vertical structure data, based on field surveys, was divided into the training/validation and test data and the hyper-parameters of ANN were trained using the training/validation data. The forest vertical classification result from ANN was evaluated by comparison to the test data. It showed about a 65.7% overall accuracy based on the error matrix. This result shows that the forest vertical structure map can be effectively generated from optical and radar satellite images and existing DEM and DTM using the ANN approach, especially for national scale mapping. Full article
(This article belongs to the Special Issue Earth Observation from KOMPSAT Optical, Thermal and Radar Satellite Images)
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17 pages, 7990 KiB  
Article
Oil Spill Mapping from Kompsat-2 High-Resolution Image Using Directional Median Filtering and Artificial Neural Network
by Sung-Hwan Park, Hyung-Sup Jung and Moung-Jin Lee
Remote Sens. 2020, 12(2), 253; https://doi.org/10.3390/rs12020253 - 10 Jan 2020
Cited by 18 | Viewed by 3474
Abstract
Oil spill accidents in marine environments have a massive impact on ecosystems. Various methods have been developed to detect oil spills using high-resolution optical imagery. However, ocean waves caused by heavy winds occurring in the accident area cause sun glint in the image, [...] Read more.
Oil spill accidents in marine environments have a massive impact on ecosystems. Various methods have been developed to detect oil spills using high-resolution optical imagery. However, ocean waves caused by heavy winds occurring in the accident area cause sun glint in the image, and this severely impedes the ability to detect the oil spill area. The objective of this study was to detect oil spill areas from high-resolution optic images using the artificial neural network (ANN) through effective suppression of severe sun glint effects. To enable this, a directional median filter (DMF) was adapted, and its use was compared with that of a traditional low-pass filter. A performance test was conducted using a KOMPSAT-2 image acquired during oil spill accidents that occurred in the Gulf of Mexico in 2010. The proposed method involved two main steps: (i) The sun glint effects caused by the ocean waves were corrected using the DMF; and (ii) the ANN approach was used to detect the oil spill area. The results show the following: (i) The designed DMF, which considers the size and angle of ocean waves, was proficient in correcting the sun glint effect in a high-resolution optical image; and (ii) oil spill areas were efficiently detected using the ANN approach with the proposed filtering method. The oil spill area was classified with accuracies of approximately 98.12% and 89.56% using the receiver operating characteristic (ROC) curve and probability of detection (POD) measurements, respectively. These results show that the accuracy of the proposed method is improved by about 9% compared to the traditional detecting algorithm. Full article
(This article belongs to the Special Issue Earth Observation from KOMPSAT Optical, Thermal and Radar Satellite Images)
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Other

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15 pages, 6685 KiB  
Technical Note
Rapid Change Detection of Flood Affected Area after Collapse of the Laos Xe-Pian Xe-Namnoy Dam Using Sentinel-1 GRD Data
by Yunjee Kim and Moung-Jin Lee
Remote Sens. 2020, 12(12), 1978; https://doi.org/10.3390/rs12121978 - 19 Jun 2020
Cited by 15 | Viewed by 3064
Abstract
Water-related disasters occur frequently worldwide and are strongly affected by a climate. Synthetic aperture radar (SAR) satellite images can be effectively used to monitor and detect damage because these images are minimally affected by weather. This study analyzed changes in water quantity and [...] Read more.
Water-related disasters occur frequently worldwide and are strongly affected by a climate. Synthetic aperture radar (SAR) satellite images can be effectively used to monitor and detect damage because these images are minimally affected by weather. This study analyzed changes in water quantity and flooded area caused by the collapse of the Xe-Pian Xe-Namnoy Dam in Laos on 23 July 2018, using Sentinel-1 ground range detected (GRD) images. The collapse of this dam gained worldwide attention and led to a large number of casualties at least 98 people, as well as enormous economic losses. Thus, it is worth noting that this study quantitatively analyzed changes in both the Hinlat area, which was flooded, and the Xe-Namnoy reservoir. This study aims to suggest a practical method of change detection which is to simply compute flood extent and water volume in rapidly analysis. At first, a α -stable distribution was fitted to intensity histogram for removing the non-water-affected pixels. This fitting differs from other typical histogram fitting methods, which is applicable to histograms with two peaks, as it can be applied to histograms with not only two peaks but also one peak. Next, another type of threshold based on digital elevation model (DEM) data was used to correct for residual noise, such as speckle noise. The results revealed that about 2.2 × 108 m3 water overflowed from the Xe-Namnoy reservoir, and a flooded area of about 28.1 km3 was detected in the Hinlat area shortly after the dam collapse. Furthermore, the water quantity and flooded area decreased in both study areas over time. Because only SAR GRD images were used in this study for rapid change detection, it is possible that more accurate results could be obtained using other available data, such as optical images with high spatial resolution like KOMPSAT-3, and in-situ data collected at the same time. Full article
(This article belongs to the Special Issue Earth Observation from KOMPSAT Optical, Thermal and Radar Satellite Images)
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