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Prof. Hyung-Sup Jung
University of seoul

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Research Keywords & Expertise

0 Data Fusion
0 GeoAI
0 Remote sensing & GIS applications
0 Satellite applications
0 Radar interferometry

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Radar interferometry
Remote sensing & GIS applications
Data Fusion

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Short Biography

Dr. Hyung-Sup Jung received the M.S. degree in geophysics in 1998 and the Ph.D. degree in remote sensing in 2007 from Yonsei University, Seoul, Korea. He is currently a Professor with the Department of Geoinfomatics at the University of Seoul. He is also serving the Korean Journal of Remote Sensing (KJRS) as an Editor-in-Chief and the IEEE Geosience and Remote Sensing Letters as an Associate Editor.

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Technical note
Published: 12 August 2021 in Remote Sensing
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It is well known that the polarization characteristics in X-band synthetic aperture radar (SAR) image analysis can provide us with additional information for marine target classification and detection. Normally, dual-and single-polarized SAR images are acquired by SAR satellites, and then we must determine how accurate the marine mapping performance from dual-polarized (pol) images is versus the marine mapping performance from the single-pol images in a given machine learning model. The purpose of this study is to compare the performance of single- and dual-pol SAR image classification achieved by the support vector machine (SVM), random forest (RF), and deep neural network (DNN) models. The test image is a TerraSAR-X dual-pol image acquired from the 2007 Kerch Strait oil spill event. For this, 824,026 pixels and 1,648,051 pixels were extracted from the image for the training and test, respectively, and sea, ship, oil, and land objects were classified from the image by using the three machine learning methods. The mean f1-scores of the SVM, RF, and DNN models resulting from the single-pol image were approximately 0.822, 0.882, and 0.889, respectively, and those from the dual-pol image were about 0.852, 0.908, and 0.898, respectively. The performance improvement achieved by dual-pol was about 3.6%, 2.9%, and 1% in SVM, RF, and DNN, respectively. The DNN model had the best performance (0.889) in the single-pol test while the RF model was best (0.908) in the dual-pol test. The performance improvement was approximately 2.1% and not noticeable. If the condition that dual-pol images have two-times lower spatial resolution versus single-pol images in the azimuth direction is considered, a small improvement may not be valuable. Therefore, the results show that the performance improvement by X-band dual-pol image may be not remarkable when classifying the sea, ships, oil spills, and sea and land surfaces.

ACS Style

Won-Kyung Baek; Hyung-Sup Jung. Performance Comparison of Oil Spill and Ship Classification from X-Band Dual- and Single-Polarized SAR Image Using Support Vector Machine, Random Forest, and Deep Neural Network. Remote Sensing 2021, 13, 3203 .

AMA Style

Won-Kyung Baek, Hyung-Sup Jung. Performance Comparison of Oil Spill and Ship Classification from X-Band Dual- and Single-Polarized SAR Image Using Support Vector Machine, Random Forest, and Deep Neural Network. Remote Sensing. 2021; 13 (16):3203.

Chicago/Turabian Style

Won-Kyung Baek; Hyung-Sup Jung. 2021. "Performance Comparison of Oil Spill and Ship Classification from X-Band Dual- and Single-Polarized SAR Image Using Support Vector Machine, Random Forest, and Deep Neural Network." Remote Sensing 13, no. 16: 3203.

Journal article
Published: 02 August 2021 in Applied Sciences
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In Korea, the Ministry of Environment and regional environment management agencies conduct environmental impact assessments (EIA) to mitigate and assess the impact of major development projects on the environment. EIA Big Data are used in conjunction with a geographical information system (GIS), and consist of indicators related to air, soil, and water that are measured before and after the development project. The impact of the development project on the environment can be evaluated through the variations of each indicator. This study analyzed trends in the environmental impacts of development projects during 2007–2016 using 21 types of EIA Big Data. A model was developed to estimate the Korean Environment Institute’s Environmental Impact Assessment Index for Development Projects (KEIDP) using a multi-layer perceptron-based artificial neural network (MLP-ANN) approach. A trend analysis of development projects in South Korea revealed that the mean value of KEIDP gradually increased over the study period. The rate of increase was 0.007 per year, with an R2 value of 0.8. In the future, it will be necessary for all management agencies to apply the KEDIP calculation model to minimize the impact of development projects on the environment and reduce deviations among development projects through continuous monitoring.

ACS Style

Sung-Hwan Park; Hyung-Sup Jung; Sunmin Lee; Heon-Seok Yoo; Nam-Wook Cho; Moung-Jin Lee. A Trend Analysis of Development Projects in South Korea during 2007–2016 Using a Multi-Layer Perceptron Based Artificial Neural Network. Applied Sciences 2021, 11, 7133 .

AMA Style

Sung-Hwan Park, Hyung-Sup Jung, Sunmin Lee, Heon-Seok Yoo, Nam-Wook Cho, Moung-Jin Lee. A Trend Analysis of Development Projects in South Korea during 2007–2016 Using a Multi-Layer Perceptron Based Artificial Neural Network. Applied Sciences. 2021; 11 (15):7133.

Chicago/Turabian Style

Sung-Hwan Park; Hyung-Sup Jung; Sunmin Lee; Heon-Seok Yoo; Nam-Wook Cho; Moung-Jin Lee. 2021. "A Trend Analysis of Development Projects in South Korea during 2007–2016 Using a Multi-Layer Perceptron Based Artificial Neural Network." Applied Sciences 11, no. 15: 7133.

Editorial
Published: 26 May 2021 in Remote Sensing
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As computer and space technologies have developed, geoscience information systems (GIS) and remote sensing (RS) techniques have also been rapidly growing

ACS Style

Hyung-Sup Jung; Saro Lee. Remote Sensing and Geoscience Information Systems Applied to Groundwater Research. Remote Sensing 2021, 13, 2086 .

AMA Style

Hyung-Sup Jung, Saro Lee. Remote Sensing and Geoscience Information Systems Applied to Groundwater Research. Remote Sensing. 2021; 13 (11):2086.

Chicago/Turabian Style

Hyung-Sup Jung; Saro Lee. 2021. "Remote Sensing and Geoscience Information Systems Applied to Groundwater Research." Remote Sensing 13, no. 11: 2086.

Editorial
Published: 04 January 2021 in Remote Sensing
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Over the past several decades, as sensor technology has improved, the spatial resolution of satellite images has been steadily improving.

ACS Style

Kwang-Jae Lee; Tae-Byeong Chae; Hyung-Sup Jung. Earth Observation from KOMPSAT Optical, Thermal, and Radar Satellite Images. Remote Sensing 2021, 13, 139 .

AMA Style

Kwang-Jae Lee, Tae-Byeong Chae, Hyung-Sup Jung. Earth Observation from KOMPSAT Optical, Thermal, and Radar Satellite Images. Remote Sensing. 2021; 13 (1):139.

Chicago/Turabian Style

Kwang-Jae Lee; Tae-Byeong Chae; Hyung-Sup Jung. 2021. "Earth Observation from KOMPSAT Optical, Thermal, and Radar Satellite Images." Remote Sensing 13, no. 1: 139.

Journal article
Published: 19 November 2020 in Applied Sciences
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In recent years, the incidence of localized heavy rainfall has increased as abnormal weather events occur more frequently. In densely populated urban areas, this type of heavy rain can cause extreme landslide damage, so that it is necessary to estimate and analyze the susceptibility of future landslides. In this regard, deep learning (DL) methodologies have been used to identify areas prone to landslides recently. Therefore, in this study, DL methodologies, including a deep neural network (DNN), kernel-based DNN, and convolutional neural network (CNN) were used to identify areas where landslides could occur. As a detailed step for this purpose, landslide occurrence was first determined as landslide inventory through aerial photographs with comparative analysis using field survey data; a training set was built for model training through oversampling based on the landslide inventory. A total of 17 landslide influencing variables that influence the frequency of landslides by topography and geomorphology, as well as soil and forest variables, were selected to establish a landslide inventory. Then models were built using DNN, kernel-based DNN, and CNN models, and the susceptibility of landslides in the study area was determined. Model performance was evaluated through the average precision (AP) score and root mean square error (RMSE) for each of the three models. Finally, DNN, kernel-based DNN, and CNN models showed performances of 99.45%, 99.44%, and 99.41%, and RMSE values of 0.1694, 0.1806, and 0.1747, respectively. As a result, all three models showed similar performance, indicating excellent predictive ability of the models developed in this study. The information of landslides occurring in urban areas, which cause a great damage even with a small number of occurrences, can provide a basis for reference to the government and local authorities for urban landslide management.

ACS Style

Sunmin Lee; Won-Kyung Baek; Hyung-Sup Jung; Saro Lee. Susceptibility Mapping on Urban Landslides Using Deep Learning Approaches in Mt. Umyeon. Applied Sciences 2020, 10, 8189 .

AMA Style

Sunmin Lee, Won-Kyung Baek, Hyung-Sup Jung, Saro Lee. Susceptibility Mapping on Urban Landslides Using Deep Learning Approaches in Mt. Umyeon. Applied Sciences. 2020; 10 (22):8189.

Chicago/Turabian Style

Sunmin Lee; Won-Kyung Baek; Hyung-Sup Jung; Saro Lee. 2020. "Susceptibility Mapping on Urban Landslides Using Deep Learning Approaches in Mt. Umyeon." Applied Sciences 10, no. 22: 8189.

Journal article
Published: 17 November 2020 in Remote Sensing
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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.

ACS Style

Kwan-Young Oh; Hyung-Sup Jung; Sung-Hwan Park; Kwang-Jae Lee. Spatial Sharpening of KOMPSAT-3A MIR Images Using Optimal Scaling Factor. Remote Sensing 2020, 12, 3772 .

AMA Style

Kwan-Young Oh, Hyung-Sup Jung, Sung-Hwan Park, Kwang-Jae Lee. Spatial Sharpening of KOMPSAT-3A MIR Images Using Optimal Scaling Factor. Remote Sensing. 2020; 12 (22):3772.

Chicago/Turabian Style

Kwan-Young Oh; Hyung-Sup Jung; Sung-Hwan Park; Kwang-Jae Lee. 2020. "Spatial Sharpening of KOMPSAT-3A MIR Images Using Optimal Scaling Factor." Remote Sensing 12, no. 22: 3772.

Journal article
Published: 02 November 2020 in Applied Sciences
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Synthetic aperture radar (SAR) images have been used in many studies for ship detection because they can be captured without being affected by time and weather. In recent years, the development of deep learning techniques has facilitated studies on ship detection in SAR images using deep learning techniques. However, because the noise from SAR images can negatively affect the learning of the deep learning model, it is necessary to reduce the noise through preprocessing. In this study, deep learning vessel detection was performed using preprocessed SAR images, and the effects of the preprocessing of the images on deep learning vessel detection were compared and analyzed. Through the preprocessing of SAR images, (1) intensity images, (2) decibel images, and (3) intensity difference and texture images were generated. The M2Det object detection model was used for the deep learning process and preprocessed SAR images. After the object detection model was trained, ship detection was performed using test images. The test results are presented in terms of precision, recall, and average precision (AP), which were 93.18%, 91.11%, and 89.78% for the intensity images, respectively, 94.16%, 94.16%, and 92.34% for the decibel images, respectively, and 97.40%, 94.94%, and 95.55% for the intensity difference and texture images, respectively. From the results, it can be found that the preprocessing of the SAR images can facilitate the deep learning process and improve the ship detection performance. The results of this study are expected to contribute to the development of deep learning-based ship detection techniques in SAR images in the future.

ACS Style

Seong-Jae Hong; Won-Kyung Baek; Hyung-Sup Jung. Ship Detection from X-Band SAR Images Using M2Det Deep Learning Model. Applied Sciences 2020, 10, 7751 .

AMA Style

Seong-Jae Hong, Won-Kyung Baek, Hyung-Sup Jung. Ship Detection from X-Band SAR Images Using M2Det Deep Learning Model. Applied Sciences. 2020; 10 (21):7751.

Chicago/Turabian Style

Seong-Jae Hong; Won-Kyung Baek; Hyung-Sup Jung. 2020. "Ship Detection from X-Band SAR Images Using M2Det Deep Learning Model." Applied Sciences 10, no. 21: 7751.

Journal article
Published: 20 October 2020 in Scientific Reports
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The artificial earthquake of mb 6.1 related to the North Korea’s sixth nuclear test occured at Mt. Mantap, North Korea on September 3, 2017. It was reported that a large and complex surface deformation was caused by the event. The surface deformation was composed of expansion of explosions, collapse, compaction and landslides. Since the precise vertical deformation measurement is very important to estimate the stability of the nuclear test facility, we retrieved a precise 3D surface deformation field and then decomposed the vertical deformation pattern from the 3D deformation. The measured maximum deformation was about − 491, − 343 and 166 cm with the measurement uncertainty of about 3.3, 4.1 and 2.7 cm in the east, north and up directions, respectively. The maximum horizontal deformation was approximately 515 cm. The horizontal deformation clearly showed a radial pattern because it was mainly caused by the explosions and landslides, while the vertical deformation displayed a rugged pattern because it was affected by the explosions, compaction and collapse. The collapse may seem to occur along the underground tunnels and at the test site’s epicenter as well. Moreover, the severe collapse was observed westside from the epicenter of the sixth nuclear test, and it has a depth of about 68.6 cm on the area of 0.3765 km2. On the basis of our results including the shapes, locations and volume changes of the large collapse, evidently a new vital piece of information was obtained so that it could be used to interprete the sixth nuclear test more accurately.

ACS Style

Won-Kyung Baek; Hyung-Sup Jung; Tae Sung Kim. Satellite radar observation of large surface collapses induced by the 2017 North Korea nuclear test. Scientific Reports 2020, 10, 1 -14.

AMA Style

Won-Kyung Baek, Hyung-Sup Jung, Tae Sung Kim. Satellite radar observation of large surface collapses induced by the 2017 North Korea nuclear test. Scientific Reports. 2020; 10 (1):1-14.

Chicago/Turabian Style

Won-Kyung Baek; Hyung-Sup Jung; Tae Sung Kim. 2020. "Satellite radar observation of large surface collapses induced by the 2017 North Korea nuclear test." Scientific Reports 10, no. 1: 1-14.

Journal article
Published: 01 October 2020 in Journal of Coastal Research
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Park, W.; Baek, W.-K.; Won, J.-S., and Jung, H.-S., 2020. Comparison of input image size for ship detection from KOMPSAT-5 SAR image using deep neural networks (DNNs). In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Geospatial Research of Coastal Environments. Journal of Coastal Research, Special Issue No. 102, pp. 208-217. Coconut Creek (Florida), ISSN 0749-0208.This study shows the results of ship detection in KOMPSAT-5 X-band SAR images using Deep Learning (DL) based the fast Deep Neural Network (DNN) method with the iterative kernel-based false alram detection algorithm. In addition, this study verifies the detection accuracy according to the size of the input data for are typical errors in SAR images such as backscattering noise, side-lobe, and ghost effect. The test results of the Fast DNN method confirmed that the error caused by the back scattering noise and side-lobe decreased as the input data size increased, however, the error for the ghost effect did not improve significantly. Also, the False Alarm Rate (FAR) was greatly improved using the iterative kernel-based false alram detection algorithm. However, caution is needed when detecting small ships because very small boats disappear. The results of this study showed that the size of the input data was very effective at 51 × 51 or higher when the fast DNN method and the iterative kernel-based false alram detection algorithm were applied to the KOMPSAT-5 images to detect ships, allowing the FAR and Intersaction over Union (IoU) showing relatively high accuracy at 0.270 and 0.611.

ACS Style

Wook Park; Won-Kyung Baek; Joong-Sun Won; Hyung-Sup Jung. Comparison of Input Image Dimensions for Ship Detection from KOMPSAT-5 SAR Image Using Deep Neural Network. Journal of Coastal Research 2020, 102, 208 -217.

AMA Style

Wook Park, Won-Kyung Baek, Joong-Sun Won, Hyung-Sup Jung. Comparison of Input Image Dimensions for Ship Detection from KOMPSAT-5 SAR Image Using Deep Neural Network. Journal of Coastal Research. 2020; 102 (sp1):208-217.

Chicago/Turabian Style

Wook Park; Won-Kyung Baek; Joong-Sun Won; Hyung-Sup Jung. 2020. "Comparison of Input Image Dimensions for Ship Detection from KOMPSAT-5 SAR Image Using Deep Neural Network." Journal of Coastal Research 102, no. sp1: 208-217.

Journal article
Published: 01 October 2020 in Journal of Coastal Research
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Baek, W.-K.; Jung, H.-S.; and Kim, D., 2020. Oil spill detection of Kerch strait in November 2007 from dual-polarized TerraSAR-X image using artificial and convolutional neural network regression models. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Geospatial Research of Coastal Environments. Journal of Coastal Research, Special Issue No. 102, pp. 137-144. Coconut Creek (Florida), ISSN 0749-0208.The oil spill is the main marine disaster. It is known that the data mining based method performs better in detecting oil than the traditional SAR based method to distinguish from lookalikes. Recently, artificial neural networks were employed to detect the oil spill. For that synthetic aperture radar intensity map, intensity texture map, co-polarized coherence map, co-polarized phase difference texture map, and digital elevation model was employed as input. The detection performance based on data mining largely depends on the used data, the characteristics of the study area, and the used model. Improving detection performance through various existing data mining models is a very important task since minor improvements in detection can have a significant impact on disaster damage mitigation. Artificial and convolutional neural network regression models were applied to detect the oil spill of Kerch strait in November 2007. The two models showed F1-scores of 0.832 and 0.823 respectively. The results of this study would contribute to monitor oil spill dynamic and mitigate damages caused by the oil spill.

ACS Style

Won-Kyung Baek; Hyung-Sup Jung; DaeSeong Kim. Oil Spill Detection of Kerch Strait in November 2007 from Dual-Polarized TerraSAR-X Image Using Artificial and Convolutional Neural Network Regression Models. Journal of Coastal Research 2020, 102, 137 -144.

AMA Style

Won-Kyung Baek, Hyung-Sup Jung, DaeSeong Kim. Oil Spill Detection of Kerch Strait in November 2007 from Dual-Polarized TerraSAR-X Image Using Artificial and Convolutional Neural Network Regression Models. Journal of Coastal Research. 2020; 102 (sp1):137-144.

Chicago/Turabian Style

Won-Kyung Baek; Hyung-Sup Jung; DaeSeong Kim. 2020. "Oil Spill Detection of Kerch Strait in November 2007 from Dual-Polarized TerraSAR-X Image Using Artificial and Convolutional Neural Network Regression Models." Journal of Coastal Research 102, no. sp1: 137-144.

Journal article
Published: 01 October 2020 in Journal of Coastal Research
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Kim, J.C.; Lee, H.J.; Choi, J.Y.; Kim, S.K. and Jung, H.S. 2020. Topographical change in coastal areas arising from soil erosion in the Riparian zone. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Geospatial Research of Coastal Environments. Journal of Coastal Research, Special Issue No. 102, pp. 101-106. Coconut Creek (Florida), ISSN 0749-0208.Sedimentary inputs into coastal estuaries because of activities upstream can lead to rapid bathymetric and topographical changes, which may affect coastal functions such as habitat or fisheries. In South Korea, the Four Major Rivers Restoration Project was initiated in 2008 to address environmental problems arising from dredging and land reclamation, and a number of ecological parks were created. This study analyzed: 1) the land cover of each period using land cover maps before (2007), immediately after the project (2012), and after five years (2017); 2) the average annual amount of soil loss from the riparian zone of the Nakdong River using the revised universal soil loss equation (RUSLE); and 3) the change in the area of barrier islands in the estuary of the Nakdong River from 2008 to 2017. In order to generate the factors required for RUSLE, various spatial data, such as land cover maps, national spatial information, aerial photographs, a soil map, and average annual precipitation data were used. The results showed the percentage of annual soil loss from classes 4, and 5 (severe and very severe levels of soil erosion, respectively) increased immediately after the project finished in 2012 but decreased by 2017. The digitized aerial photographs confirmed that the area of Maenggeummeori-do, one of the barrier islands located in the estuary of the Nakdong River, decreased by more than 20 % as sediment inputs declined. Thus, it was confirmed that changes in soil loss in riparian zones arising from river maintenance projects can affect the area of barrier islands in coastal estuaries. Through this study, it was revealed that large-scale river refurbishment projects indirectly affected soil erosion in the waterfront area of the Nakdong River, which eventually affected the area change of the barrier island at the mouth of the Nakdong River.

ACS Style

Jeong-Cheol Kim; Hyeon-Jeong Lee; Jong-Yun Choi; Seong-Ki Kim; Hyung-Sup Jung. Topographical Change in Coastal Areas Arising from Soil Erosion in the Riparian Zone. Journal of Coastal Research 2020, 102, 101 -106.

AMA Style

Jeong-Cheol Kim, Hyeon-Jeong Lee, Jong-Yun Choi, Seong-Ki Kim, Hyung-Sup Jung. Topographical Change in Coastal Areas Arising from Soil Erosion in the Riparian Zone. Journal of Coastal Research. 2020; 102 (sp1):101-106.

Chicago/Turabian Style

Jeong-Cheol Kim; Hyeon-Jeong Lee; Jong-Yun Choi; Seong-Ki Kim; Hyung-Sup Jung. 2020. "Topographical Change in Coastal Areas Arising from Soil Erosion in the Riparian Zone." Journal of Coastal Research 102, no. sp1: 101-106.

Journal article
Published: 03 July 2020 in Engineering
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Conventional synthetic aperture radar (SAR) interferometry (InSAR) has been successfully used to precisely measure surface deformation in the line-of-sight (LOS) direction, while multiple-aperture SAR interferometry (MAI) has provided precise surface deformation in the along-track (AT) direction. Integration of the InSAR and MAI methods enables precise measurement of the two-dimensional (2D) deformation from an interferometric pair; recently, the integration of ascending and descending pairs has allowed the observation of precise three-dimensional (3D) deformation. Precise 3D deformation measurement has been applied to better understand geological events such as earthquakes and volcanic eruptions. The surface deformation related to the 2016 Kumamoto earthquake was large and complex near the fault line; hence, precise 3D deformation retrieval had not yet been attempted. The objectives of this study were to (1) perform a feasibility test of precise 3D deformation retrieval in large and complex deformation areas though the integration of offset-based unwrapped and improved multiple-aperture SAR interferograms and (2) observe the 3D deformation field related to the 2016 Kumamoto earthquake, even near the fault lines. Two ascending pairs and one descending the Advanced Land Observing Satellite-2 (ALOS2) the Phased Array-type L-band Synthetic Aperture Radar-2 (PALSAR2) pair were used for the 3D deformation retrieval. Eleven in situ Global Positioning System (GPS) measurements were used to validate the 3D deformation measurement accuracy. The achieved accuracy was approximately 2.96, 3.75, and 2.86 cm in the east, north, and up directions, respectively. The results show the feasibility of precise 3D deformation measured through the integration of the improved methods, even in a case of large and complex deformation.

ACS Style

Won-Kyung Baek; Hyung-Sup Jung. Precise Three-Dimensional Deformation Retrieval in Large and Complex Deformation Areas via Integration of Offset-Based Unwrapping and Improved Multiple-Aperture SAR Interferometry: Application to the 2016 Kumamoto Earthquake. Engineering 2020, 6, 927 -935.

AMA Style

Won-Kyung Baek, Hyung-Sup Jung. Precise Three-Dimensional Deformation Retrieval in Large and Complex Deformation Areas via Integration of Offset-Based Unwrapping and Improved Multiple-Aperture SAR Interferometry: Application to the 2016 Kumamoto Earthquake. Engineering. 2020; 6 (8):927-935.

Chicago/Turabian Style

Won-Kyung Baek; Hyung-Sup Jung. 2020. "Precise Three-Dimensional Deformation Retrieval in Large and Complex Deformation Areas via Integration of Offset-Based Unwrapping and Improved Multiple-Aperture SAR Interferometry: Application to the 2016 Kumamoto Earthquake." Engineering 6, no. 8: 927-935.

Editorial
Published: 19 March 2020 in Sustainability
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The Special Issue on “Sustainable Applications of Remote Sensing and Geospatial Information Systems to Earth Observations” is published. A total of 20 qualified papers are published in this Special Issue. The topics of the papers are the application of remote sensing and geospatial information systems to Earth observations in various fields such as (1) object change detection, (2) air pollution, (3) earthquakes, (4) landslides, (5) mining, (6) biomass, (7) groundwater, and (8) urban development using the techniques of remote sensing and geospatial information systems. More than 100 researchers have participated in this Special Issue. We hope that this Special Issue is helpful for sustainable applications.

ACS Style

Hyung-Sup Jung; Saro Lee; Biswajeet Pradhan. Sustainable Applications of Remote Sensing and Geospatial Information Systems to Earth Observations. Sustainability 2020, 12, 2390 .

AMA Style

Hyung-Sup Jung, Saro Lee, Biswajeet Pradhan. Sustainable Applications of Remote Sensing and Geospatial Information Systems to Earth Observations. Sustainability. 2020; 12 (6):2390.

Chicago/Turabian Style

Hyung-Sup Jung; Saro Lee; Biswajeet Pradhan. 2020. "Sustainable Applications of Remote Sensing and Geospatial Information Systems to Earth Observations." Sustainability 12, no. 6: 2390.

Journal article
Published: 02 March 2020 in Remote Sensing
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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.

ACS Style

Yong-Suk Lee; Sunmin Lee; Won-Kyung Baek; Hyung-Sup Jung; Sung-Hwan Park; Moung-Jin Lee. Mapping Forest Vertical Structure in Jeju Island from Optical and Radar Satellite Images Using Artificial Neural Network. Remote Sensing 2020, 12, 797 .

AMA Style

Yong-Suk Lee, Sunmin Lee, Won-Kyung Baek, Hyung-Sup Jung, Sung-Hwan Park, Moung-Jin Lee. Mapping Forest Vertical Structure in Jeju Island from Optical and Radar Satellite Images Using Artificial Neural Network. Remote Sensing. 2020; 12 (5):797.

Chicago/Turabian Style

Yong-Suk Lee; Sunmin Lee; Won-Kyung Baek; Hyung-Sup Jung; Sung-Hwan Park; Moung-Jin Lee. 2020. "Mapping Forest Vertical Structure in Jeju Island from Optical and Radar Satellite Images Using Artificial Neural Network." Remote Sensing 12, no. 5: 797.

Journal article
Published: 01 March 2020 in Applied Sciences
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As global warming accelerates in recent years, the frequency of droughts has increased and water management at the national level has become very important. In particular, accurate understanding and management of the forest is essential as the water storage capacity of forest is determined by forest structure. Typically, data on forest vertical structure have been constructed from field surveys that are both costly and time-consuming. In addition, machine learning techniques could be applied to analyze, classify, and predict the uncertainties of internal structures in forest. Therefore, this study aims to map the forest vertical structure for estimating forest water storage capacity from multi-seasonal optical satellite image and topographic data using artificial neural network (ANN) in Gongju-si, South Korea. For this purpose, the 14 input neurons of normalized difference vegetation index (NDVI), two types of normalized difference water index (NDWI), two types of Normalized Difference Red Edge Index (NDre), principal component analysis (PCA) texture, and canopy height average and standard deviation maps were generated from Sentinel-2 optical images obtained in spring and fall season and topographic height maps such as digital terrain models (DTM) and digital surface models (DSM). The training/validation and test datasets for the ANN model were derived from forest vertical structures based on field surveys. Finally, the forest vertical classification map, the result of ANN application, was evaluated by creating an error matrix compared with the field survey results. The result showed an overall test accuracy of ~65.7% based on the number of pixels. The result shows that forest vertical structure in Gong-ju, Korea can be efficiently classified by using multi-seasonal Sentinel-2 satellite images and the ANN approach.

ACS Style

Yong-Suk Lee; Sunmin Lee; Hyung-Sup Jung. Mapping Forest Vertical Structure in Gong-ju, Korea Using Sentinel-2 Satellite Images and Artificial Neural Networks. Applied Sciences 2020, 10, 1666 .

AMA Style

Yong-Suk Lee, Sunmin Lee, Hyung-Sup Jung. Mapping Forest Vertical Structure in Gong-ju, Korea Using Sentinel-2 Satellite Images and Artificial Neural Networks. Applied Sciences. 2020; 10 (5):1666.

Chicago/Turabian Style

Yong-Suk Lee; Sunmin Lee; Hyung-Sup Jung. 2020. "Mapping Forest Vertical Structure in Gong-ju, Korea Using Sentinel-2 Satellite Images and Artificial Neural Networks." Applied Sciences 10, no. 5: 1666.

Journal article
Published: 10 January 2020 in Remote Sensing
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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.

ACS Style

Sung-Hwan Park; Hyung-Sup Jung; Moung-Jin Lee. Oil Spill Mapping from Kompsat-2 High-Resolution Image Using Directional Median Filtering and Artificial Neural Network. Remote Sensing 2020, 12, 253 .

AMA Style

Sung-Hwan Park, Hyung-Sup Jung, Moung-Jin Lee. Oil Spill Mapping from Kompsat-2 High-Resolution Image Using Directional Median Filtering and Artificial Neural Network. Remote Sensing. 2020; 12 (2):253.

Chicago/Turabian Style

Sung-Hwan Park; Hyung-Sup Jung; Moung-Jin Lee. 2020. "Oil Spill Mapping from Kompsat-2 High-Resolution Image Using Directional Median Filtering and Artificial Neural Network." Remote Sensing 12, no. 2: 253.

Journal article
Published: 30 December 2019 in IEEE Access
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Sung-Hwan Park; Hyung-Sup Jung. Band-Based Best Model Selection for Topographic Normalization of Normalized Difference Vegetation Index Map. IEEE Access 2019, 8, 4408 -4417.

AMA Style

Sung-Hwan Park, Hyung-Sup Jung. Band-Based Best Model Selection for Topographic Normalization of Normalized Difference Vegetation Index Map. IEEE Access. 2019; 8 ():4408-4417.

Chicago/Turabian Style

Sung-Hwan Park; Hyung-Sup Jung. 2019. "Band-Based Best Model Selection for Topographic Normalization of Normalized Difference Vegetation Index Map." IEEE Access 8, no. : 4408-4417.

Journal article
Published: 30 September 2019 in Remote Sensing
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This study analyzed the Groundwater Productivity Potential (GPP) of Okcheon city, Korea, using three different models. Two of these three models are data mining models: Boosted Regression Tree (BRT) model and Random Forest (RF) model. The other model is the Logistic Regression (LR) model. The three models are based on the relationship between groundwater-productivity data (specific capacity (SPC) and transmissivity (T)) and the related hydro-geological factors from thematic maps, such as topography, lineament, geology, land cover, and etc. The thematic maps which are generated from the remote sensing images. Groundwater productivity data were collected from 86 wells locations. The resulting GPP maps were validated through area-under-the-curve (AUC) analysis using wells data that had not been used for training the model. When T was used in the BRT, RF, and LR models, the obtained GPP maps had 81.66%, 80.21%, and 85.04% accuracy, respectively, and when SPC was used, the maps had 81.53%, 78.57%, and 82.22% accuracy, respectively. The LR model, which is a statistical model, showed the highest verification accuracy, also the other two models showed high accuracies. These observations indicate that all three models can be useful for groundwater resource development.

ACS Style

Jeong-Cheol Kim; Hyung-Sup Jung; Saro Lee. Spatial Mapping of the Groundwater Potential of the Geum River Basin Using Ensemble Models Based on Remote Sensing Images. Remote Sensing 2019, 11, 2285 .

AMA Style

Jeong-Cheol Kim, Hyung-Sup Jung, Saro Lee. Spatial Mapping of the Groundwater Potential of the Geum River Basin Using Ensemble Models Based on Remote Sensing Images. Remote Sensing. 2019; 11 (19):2285.

Chicago/Turabian Style

Jeong-Cheol Kim; Hyung-Sup Jung; Saro Lee. 2019. "Spatial Mapping of the Groundwater Potential of the Geum River Basin Using Ensemble Models Based on Remote Sensing Images." Remote Sensing 11, no. 19: 2285.

Journal article
Published: 15 September 2019 in Applied Sciences
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Spaceborne synthetic aperture radar (SAR) imagery is affected by the ionosphere, resulting in distortions of the SAR intensity, phase, and polarization. Although several methods have been proposed to mitigate the ionospheric phase delay of SAR interferometry, the application of them with full-polarimetric SAR interferometry is limited. Based on this background, Faraday rotation (FR)-based methods are used in this study to mitigate the ionospheric phase errors on full-polarimetric SAR interferometry. For a performance test of the selected method, L-band Advanced Land Observation Satellite (ALOS) Phase Array L-band SAR (PALSAR) full-polarimetric SAR images over high-latitude and low-latitude regions are processed. The result shows that most long-wavelength ionospheric phase errors are removed from the original phase after using the FR-based method, where standard deviations of the corrected result have decreased by almost a factor of eight times for the high-latitude region and 28 times for low-latitude region, compared to those of the original phase, demonstrating the efficiency of the method. This result proves that the FR-based method not only can mitigate the ionospheric effect on SAR interferometry, but also can map the high-spatial-resolution vertical total electronic content (VTEC) distribution.

ACS Style

Wu Zhu; Hyung-Sup Jung; Jing-Yuan Chen. Synthetic Aperture Radar Interferometry (InSAR) Ionospheric Correction Based on Faraday Rotation: Two Case Studies. Applied Sciences 2019, 9, 3871 .

AMA Style

Wu Zhu, Hyung-Sup Jung, Jing-Yuan Chen. Synthetic Aperture Radar Interferometry (InSAR) Ionospheric Correction Based on Faraday Rotation: Two Case Studies. Applied Sciences. 2019; 9 (18):3871.

Chicago/Turabian Style

Wu Zhu; Hyung-Sup Jung; Jing-Yuan Chen. 2019. "Synthetic Aperture Radar Interferometry (InSAR) Ionospheric Correction Based on Faraday Rotation: Two Case Studies." Applied Sciences 9, no. 18: 3871.

Journal article
Published: 09 September 2019 in IEEE Access
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For the measurement of abrupt and large surface movements caused by earthquakes, volcanic eruption and melting glacier, Synthetic aperture radar (SAR) offset tracking method would be a feasible solution because it can provide unambiguous ground displacements in both the ground range and azimuth directions when the interferometric phase is not coherent. However, the measurement performance of the method largely depends on the kernel size, which denotes the size of search window to estimate the azimuth and range offsets between reference and target SAR images. Thus, there is a trade-off between sensitivity and measurement density depending on the search kernel size. In this study, an enhanced SAR offset tracking method based on multi-kernel processing has been developed to find an optimized measurement from the trade-off between resolution and measurement accuracy. It can obtain optimal surface displacement measurements by calculating multiple offset measurements and determining a final measurement from the statistical properties of the multiple measurements. The measurement performance of the proposed method was evaluated by using European Remote Sensing 2 (ERS-2) satellite SAR data sets of the Hector Mine earthquake event in 1999 and Advanced Land Observing Satellite-2 Phased Array type L-band Synthetic Aperture Radar-2 (ALOS-2 PALSAR-2) data sets of the 2016 Kumamoto earthquake event. Our results showed that an optimized measurement from the trade-off between the observation accuracy and resolution can be effectively determined by our proposed processing strategy. The results are improved results for measurement density and accuracy over previously published results. It further confirmed that our new method is allowed for the optimal measuring the large-scale fast surface displacements that cannot be sufficiently observed with the phase-based SAR method.

ACS Style

Sung-Ho Chae; Won-Jin Lee; Won-Kyung Baek; Hyung-Sup Jung. An Improvement of the Performance of SAR Offset Tracking Approach to Measure Optimal Surface Displacements. IEEE Access 2019, 7, 131627 -131637.

AMA Style

Sung-Ho Chae, Won-Jin Lee, Won-Kyung Baek, Hyung-Sup Jung. An Improvement of the Performance of SAR Offset Tracking Approach to Measure Optimal Surface Displacements. IEEE Access. 2019; 7 (99):131627-131637.

Chicago/Turabian Style

Sung-Ho Chae; Won-Jin Lee; Won-Kyung Baek; Hyung-Sup Jung. 2019. "An Improvement of the Performance of SAR Offset Tracking Approach to Measure Optimal Surface Displacements." IEEE Access 7, no. 99: 131627-131637.