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Selected Papers from the “International Symposium on Remote Sensing 2021”

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

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 46495

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Prof. Dr. Hyung-Sup Jung

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1. Department of Smart Cities, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02120, Korea
2. Department of Geoinformatics, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02120, Korea
Interests: remote sensing; geoinformatics; satellite data processing; environmental sensing
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Dr. Sang-Hoon Hong

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Department of Geological Sciences, Pusan National University, Pusan 46241, Republic of Korea
Interests: synthetic aperture radar; radar interferometry; surface displacement; glacier; sea ice; wetlands; geodesy; hydrology
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Jin-Soo Kim

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Guest Editor

Department of Spatial Information System, Pukyong National University, 45 Yongso-ro, Busan 48513, Korea
Interests: semantic segmentation based on deep learning; land use/land cover change modeling; disaster vulnerability mapping

Special Issue Information

Dear Colleagues,

The International Symposium on Remote Sensing 2021 (ISRS 2021, http://isrs.or.kr/) will be a fully virtual meeting to provide all members of our community with the opportunity to participate in the annual ISRS event. 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 2021 and will include peer-reviewed feature papers presented at ISRS 2021. Remote Sensing is an open access journal about the science and applications of remote sensing technology and is published online. The ISRS 2021 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 2021. If this information is not provided, the paper will not be considered as a Special Issue paper.

Prof.Dr. Hyung-Sup Jung
Prof.Dr. Sang-Hoon Hong
Prof.Dr. Jin-Soo Kim
Guest Editors

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Keywords

International Symposium on Remote Sensing 2021
Remote sensing
Geoinformatics
Geoscience information system (GIS)
Global positioning system (GPS)
Image processing

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Published Papers (12 papers)

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Editorial

Jump to: Research, Other

3 pages, 170 KiB

Open AccessEditorial

Special Issue on Selected Papers from “International Symposium on Remote Sensing 2021”

by Sang-Hoon Hong, Jinsoo Kim and Hyung-Sup Jung

Remote Sens. 2023, 15(12), 2993; https://doi.org/10.3390/rs15122993 - 8 Jun 2023

Cited by 1 | Viewed by 1485

Abstract

The International Symposium on Remote Sensing 2021 (ISRS 2021) was held as a fully virtual meeting to provide all members of our community with the opportunity to participate in the annual ISRS event [...] Full article

(This article belongs to the Special Issue Selected Papers from the “International Symposium on Remote Sensing 2021”)

Research

Jump to: Editorial, Other

18 pages, 4937 KiB

Open AccessArticle

Reproduction of the Marine Debris Distribution in the Seto Inland Sea Immediately after the July 2018 Heavy Rains in Western Japan Using Multidate Landsat-8 Data

by Shilin Song, Yuji Sakuno, Naokazu Taniguchi and Hidetsugu Iwashita

Remote Sens. 2021, 13(24), 5048; https://doi.org/10.3390/rs13245048 - 12 Dec 2021

Cited by 8 | Viewed by 3743

Abstract

Understanding the spatiotemporal environment of the ocean after a heavy rain disaster is critical for satellite remote sensing research and disaster prevention. We attempted to reproduce changes in marine debris distributions using multidate data of Landsat-8 spectral reflectance acquired immediately after a heavy rain disaster in western Japan in July 2018. Data from cleaning ships were used for screening the marine debris area. As most of the target marine debris consisted of plant fragments, a method based on the corrected floating algae index (cFAI) was applied to Landsat-8 data. Data from cleaning ships clarify that most of the marine debris accumulated in the waters in the northern part of Aki Nada, a part of the Seto Inland Sea. The spectral characteristics of the corresponding marine debris spectral reflectance obtained from the Landsat-8 data were explained by the FAI with band 5 (central wavelength: 865 nm) as the maximum value. Unlike traditional FAI, cFAI eliminated the effect of background water turbidity. The Otsu method was effective for the automatic threshold determination for cFAI. Although Landsat-8 data have limited spatial resolution and observation frequency, these data were useful for understanding marine debris distribution after a heavy rain disaster. Full article

(This article belongs to the Special Issue Selected Papers from the “International Symposium on Remote Sensing 2021”)

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17 pages, 3786 KiB

Open AccessArticle

Near-Surface Air Temperature Retrieval Using a Deep Neural Network from Satellite Observations over South Korea

by Sungwon Choi, Donghyun Jin, Noh-Hun Seong, Daeseong Jung, Suyoung Sim, Jongho Woo, Uujin Jeon, Yugyeong Byeon and Kyung-soo Han

Remote Sens. 2021, 13(21), 4334; https://doi.org/10.3390/rs13214334 - 28 Oct 2021

Cited by 7 | Viewed by 2601

Abstract

Air temperature (Ta), defined as the temperature 2 m above the land’s surface, is one of the most important factors for environment and climate studies. Ta can be measured by obtaining the land surface temperature (LST) which can be retrieved with the 11- and 12-µm bands from satellite imagery over a large area, and LST is highly correlated with Ta. To measure the Ta in a broad area, we studied a Ta retrieval method through Deep Neural Network (DNN) using in-situ data and satellite data of South Korea from 2014 to 2017. To retrieve accurate Ta, we selected proper input variables and conditions of a DNN model. As a result, Normalized Difference Vegetation Index, Normalized Difference Water Index, and 11- and 12-µm band data were applied to the DNN model as input variables. And we also selected proper condition of the DNN model with test various conditions of the model. In validation result in the DNN model, the best accuracy of the retrieved Ta showed an correlation coefficient value of 0.98 and a root mean square error (RMSE) of 2.19 K. And then we additional 3 analysis to validate accuracy which are spatial representativeness, seasonal analysis and time series analysis. We tested the spatial representativeness of the retrieved Ta. Results for window sizes less than 132 × 132 showed high accuracy, with a correlation coefficient of over 0.97 and a RMSE of 1.96 K and a bias of −0.00856 K. And in seasonal analysis, the spring season showed the lowest accuracy, 2.82 K RMSE value, other seasons showed high accuracy under 2K RMSE value. We also analyzed a time series of six the Automated Synoptic Observing System (ASOS) points (i.e., locations) using data obtained from 2018 to 2019; all of the individual correlation coefficient values were over 0.97 and the RMSE values were under 2.41 K. With these analysis, we confirm accuracy of the DNN model was higher than previous studies. And we thought the retrieved Ta can be used in other studies or climate model to conduct urban problems like urban heat islands and to analyze effects of arctic oscillation. Full article

(This article belongs to the Special Issue Selected Papers from the “International Symposium on Remote Sensing 2021”)

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15 pages, 21436 KiB

Open AccessArticle

Forest Vertical Structure Mapping Using Two-Seasonal Optic Images and LiDAR DSM Acquired from UAV Platform through Random Forest, XGBoost, and Support Vector Machine Approaches

by Jin-Woo Yu, Young-Woong Yoon, Won-Kyung Baek and Hyung-Sup Jung

Remote Sens. 2021, 13(21), 4282; https://doi.org/10.3390/rs13214282 - 25 Oct 2021

Cited by 39 | Viewed by 4267

Abstract

Research on the forest structure classification is essential, as it plays an important role in assessing the vitality and diversity of vegetation. However, classifying forest structure involves in situ surveying, which requires considerable time and money, and cannot be conducted directly in some instances; also, the update cycle of the classification data is very late. To overcome these drawbacks, feasibility studies on mapping the forest vertical structure from aerial images using machine learning techniques were conducted. In this study, we investigated (1) the performance improvement of the forest structure classification, using a high-resolution LiDAR-derived digital surface model (DSM) acquired from an unmanned aerial vehicle (UAV) platform and (2) the performance comparison of results obtained from the single-seasonal and two-seasonal data, using random forest (RF), extreme gradient boosting (XGBoost), and support vector machine (SVM). For the performance comparison, the UAV optic and LiDAR data were divided into three cases: (1) only used autumn data, (2) only used winter data, and (3) used both autumn and winter data. From the results, the best model was XGBoost, and the F1 scores achieved using this method were approximately 0.92 in the autumn and winter cases. A remarkable improvement was achieved when both two-seasonal images were used. The F1 score improved by 35.3% from 0.68 to 0.92. This implies that (1) the seasonal variation in the forest vertical structure can be more important than the spatial resolution, and (2) the classification performance achieved from the two-seasonal UAV optic images and LiDAR-derived DSMs can reach 0.9 with the application of an optimal machine learning approach. Full article

(This article belongs to the Special Issue Selected Papers from the “International Symposium on Remote Sensing 2021”)

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21 pages, 4009 KiB

Open AccessArticle

Improved Calibration of Wind Estimates from Advanced Scatterometer MetOp-B in Korean Seas Using Deep Neural Network

by Sung-Hwan Park, Jeseon Yoo, Donghwi Son, Jinah Kim and Hyung-Sup Jung

Remote Sens. 2021, 13(20), 4164; https://doi.org/10.3390/rs13204164 - 18 Oct 2021

Cited by 7 | Viewed by 2667

Abstract

Satellite-based observations of sea wind are useful for forecasting marine weather and performing marine disaster management. Meteorological Operational Satellite-B (MetOp-B) is one of the satellites that provide wind products through a scatterometer named the Advanced Scatterometer (ASCAT). Since the linear regression method has been conventionally employed for calibrating remotely-sensed wind data, deviations and biases remain un-resolved to some degree. For coastal applications, these remotely-sensed wind data need to be calibrated again using local in-situ measurements in order to provide more accurate and realistic information. Thus, this study proposed a new method to calibrate ASCAT-based wind speed estimates using artificial neural networks. Herein, a deep neural network (DNN) model was applied. Wind databases collected during a period from 2012 to 2019 by the MetOp-B ASCAT and ten buoy stations in Korean seas were considered for deep learning-based calibration. ASCAT-based wind data and in-situ measurements were collocated in space and time. They were then separated into training and validation sets. A DNN model was designed and trained using multiple input variables such as observation location, sensing date and time, wind speed, and wind direction of the training set. The DNN-based best fit calibration model was evaluated using the validation set. The mean of biases between ASCAT-based and in-situ wind speeds was found to be decreased from 0.41 to 0.05 m/s on average for all buoy locations. The root mean squared error (RMSE) of wind speed was reduced from 1.38 m/s to 0.93 m/s. Moreover, the DNN-based calibration considerably improved the quality of wind speeds of less than 4 m/s, and of high wind speeds of 11–25 m/s. These results suggest that ASCAT-based observations can accurately represent real wind fields if a DNN-based calibration approach is applied. Full article

(This article belongs to the Special Issue Selected Papers from the “International Symposium on Remote Sensing 2021”)

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21 pages, 37571 KiB

Open AccessArticle

Mapping Forest Vertical Structure in Sogwang-ri Forest from Full-Waveform Lidar Point Clouds Using Deep Neural Network

by Sung-Hwan Park, Hyung-Sup Jung, Sunmin Lee and Eun-Sook Kim

Remote Sens. 2021, 13(18), 3736; https://doi.org/10.3390/rs13183736 - 17 Sep 2021

Cited by 11 | Viewed by 3136

Abstract

The role of forests is increasing because of rapid land use changes worldwide that have implications on ecosystems and the carbon cycle. Therefore, it is necessary to obtain accurate information about forests and build forest inventories. However, it is difficult to assess the internal structure of the forest through 2D remote sensing techniques and fieldwork. In this aspect, we proposed a method for estimating the vertical structure of forests based on full-waveform light detection and ranging (FW LiDAR) data in this study. Voxel-based tree point density maps were generated by estimating the number of canopy height points in each voxel grid from the raster digital terrain model (DTM) and canopy height points after pre-processing the LiDAR point clouds. We applied an unsupervised classification algorithm to the voxel-based tree point density maps and identified seven classes by profile pattern analysis for the forest vertical types. The classification accuracy was found to be 72.73% from the validation from 11 field investigation sites, which was additionally confirmed through comparative analysis with aerial images. Based on this pre-classification reference map, which is assumed to be ground truths, the deep neural network (DNN) model was finally applied to perform the final classification. As a result of accuracy assessment, it showed accuracy of 92.72% with a good performance. These results demonstrate the potential of vertical structure estimation for extensive forests using FW LiDAR data and that the distinction between one-storied and two-storied forests can be clearly represented. This technique is expected to contribute to efficient and effective management of forests based on accurate information derived from the proposed method. Full article

(This article belongs to the Special Issue Selected Papers from the “International Symposium on Remote Sensing 2021”)

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21 pages, 11728 KiB

Open AccessArticle

Daytime Cloud Detection Algorithm Based on a Multitemporal Dataset for GK-2A Imagery

by Soobong Lee and Jaewan Choi

Remote Sens. 2021, 13(16), 3215; https://doi.org/10.3390/rs13163215 - 13 Aug 2021

Cited by 17 | Viewed by 3716

Abstract

Cloud detection is an essential and important process in remote sensing when surface information is required for various fields. For this reason, we developed a daytime cloud detection algorithm for GEOstationary KOrea Multi-Purpose SATellite 2A (GEO-KOMPSAT-2A, GK-2A) imagery. For each pixel, the filtering technique using angular variance, which denotes the change in top of atmosphere (TOA) reflectance over time, was applied, and filtering technique by using the minimum TOA reflectance was used to remove remaining cloud pixels. Furthermore, near-infrared (NIR) and normalized difference vegetation index (NDVI) images were applied with dynamic thresholds to improve the accuracy of the cloud detection results. The quantitative results showed that the overall accuracy of proposed cloud detection was 0.88 and 0.92 with Visible Infrared Imaging Radiometer Suite (VIIRS) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), respectively, and indicated that the proposed algorithm has good performance in detecting clouds. Full article

(This article belongs to the Special Issue Selected Papers from the “International Symposium on Remote Sensing 2021”)

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19 pages, 11384 KiB

Open AccessArticle

Characteristic Analysis of Sea Surface Currents around Taiwan Island from CODAR Observations

by Yu-Hao Tseng, Ching-Yuan Lu, Quanan Zheng and Chung-Ru Ho

Remote Sens. 2021, 13(15), 3025; https://doi.org/10.3390/rs13153025 - 1 Aug 2021

Cited by 15 | Viewed by 5376

Abstract

Sea surface currents observed by high-frequency (HF) radars have been widely used in ocean circulation research. In this study, hourly sea surface currents observed by the Taiwan Coastal Ocean Dynamics Applications Radar (CODAR) system from 2015 to 2019 were analyzed by the empirical orthogonal function (EOF) analysis to reveal the characteristics of the sea surface currents around Taiwan Island. The study area is divided into two regions, the Kuroshio region east of Taiwan Island and the Taiwan Strait west of Taiwan Island. In the Kuroshio region, the first EOF mode shows that the Kuroshio is characterized by higher current speeds with greater variability in summer. The second and third EOF modes present a dipole eddy pair and single eddy impingement on the Kuroshio during different periods. The seasonal variation of the dipole eddy pair indicates that the cyclonic/anticyclonic eddy on the north/south side appears more frequently in summer. Single eddy impingement occurs at multiple periods, including daily, intraseasonal, interseasonal, and annual periods. For the Taiwan Strait, the first EOF mode displays the tide signals. The tides enter the Taiwan Strait from the north and south, forming strong sea surface currents around the northern tip of Taiwan Island and the Penghu Archipelago. The second EOF mode exhibits the seasonal changes of the sea surface currents driven by the monsoon winds. The sea surface currents in the northern Taiwan Strait are relatively strong, possibly due to the narrow and shallow terrain there. The high spatiotemporal resolution of sea surface currents derived from CODAR observations provide more detailed characteristics of sea surface circulation around Taiwan Island. Full article

(This article belongs to the Special Issue Selected Papers from the “International Symposium on Remote Sensing 2021”)

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25 pages, 32847 KiB

Open AccessArticle

Analysis of Activity in an Open-Pit Mine by Using InSAR Coherence-Based Normalized Difference Activity Index

by Jihyun Moon and Hoonyol Lee

Remote Sens. 2021, 13(9), 1861; https://doi.org/10.3390/rs13091861 - 10 May 2021

Cited by 15 | Viewed by 4447

Abstract

In this study, time-series of Sentinel-1A/B Interferometric Synthetic Aperture Radar (InSAR) coherence images were used to monitor the mining activity of Musan open-pit mine, the largest iron mine in North Korea. First, the subtraction of SRTM DEM (2000) from TanDEM-X DEM (2010–2015) has identified two major accumulation areas, one in the east (+112.33 m) and the other in the west (+84.03 m), and a major excavation area (−42.54 m) at the center of the mine. A total of 89 high-quality coherence images with a 12-day baseline from 2015 to 2020 were converted to the normalized difference activity index (NDAI), a newly developed activity indicator robust to spatial and temporal decorrelation. An RGB composite of annually averaged NDAI maps (red for 2019, green for 2018, and blue for 2017) showed that overall activity has diminished since 2018. Dumping slopes were categorized into shrinking, expanding, or transitional, according to the color pattern. Migration and expansion of excavation sites were also found on the pit floor. Time series of 12-day NDAI graphs revealed the date of activities with monthly accuracy. It is believed that NDAI with continuous acquisition of Sentinel-1A/B data can provide detailed monitoring of various types of activities in open-pit mines especially with limited in situ data. Full article

(This article belongs to the Special Issue Selected Papers from the “International Symposium on Remote Sensing 2021”)

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21 pages, 6815 KiB

Open AccessArticle

Potential of Hybrid CNN-RF Model for Early Crop Mapping with Limited Input Data

by Geun-Ho Kwak, Chan-won Park, Kyung-do Lee, Sang-il Na, Ho-yong Ahn and No-Wook Park

Remote Sens. 2021, 13(9), 1629; https://doi.org/10.3390/rs13091629 - 21 Apr 2021

Cited by 45 | Viewed by 5253

Abstract

When sufficient time-series images and training data are unavailable for crop classification, features extracted from convolutional neural network (CNN)-based representative learning may not provide useful information to discriminate crops with similar spectral characteristics, leading to poor classification accuracy. In particular, limited input data are the main obstacles to obtain reliable classification results for early crop mapping. This study investigates the potential of a hybrid classification approach, i.e., CNN-random forest (CNN-RF), in the context of early crop mapping, that combines the automatic feature extraction capability of CNN with the superior discrimination capability of an RF classifier. Two experiments on incremental crop classification with unmanned aerial vehicle images were conducted to compare the performance of CNN-RF with that of CNN and RF with respect to the length of the time-series and training data sizes. When sufficient time-series images and training data were used for the classification, the accuracy of CNN-RF was slightly higher or comparable with that of CNN. In contrast, when fewer images and the smallest training data were used at the early crop growth stage, CNN-RF was substantially beneficial and the overall accuracy increased by maximum 6.7%p and 4.6%p in the two study areas, respectively, compared to CNN. This is attributed to its ability to discriminate crops from features with insufficient information using a more sophisticated classifier. The experimental results demonstrate that CNN-RF is an effective classifier for early crop mapping when only limited input images and training samples are available. Full article

(This article belongs to the Special Issue Selected Papers from the “International Symposium on Remote Sensing 2021”)

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19 pages, 12734 KiB

Open AccessArticle

Robust Maritime Target Detector in Short Dwell Time

by Myung-Jun Lee, Ji-Eun Kim, Bo-Hyun Ryu and Kyung-Tae Kim

Remote Sens. 2021, 13(7), 1319; https://doi.org/10.3390/rs13071319 - 30 Mar 2021

Cited by 9 | Viewed by 2855

Abstract

Detection of small-sized maritime targets is an important task for a marine surveillance radar. Recently, with the emergence of a marine surveillance radar system that has a narrow azimuth beamwidth and rapidly rotating antennas, the available dwell time for detecting a maritime target is usually very short. This short dwell time considerably degrades the performance of conventional detectors, especially those focusing on small-sized targets. In this paper, we propose an efficient detector for small-sized maritime targets to provide a reliable detection performance, even in short dwell times. The proposed scheme is based on a new joint metric, which results from the product of the magnitude and difference features in the Doppler spectra. We discriminate the target bins from sea clutter bins using a statistical discriminator based on the joint metric, whose probability density function follows the product distribution of standard gamma distributions. Compared to conventional detectors, the proposed scheme can provide a robust performance in terms of the average signal-to-clutter ratio as well as the detection rate, especially in shorter dwell times. Full article

(This article belongs to the Special Issue Selected Papers from the “International Symposium on Remote Sensing 2021”)

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16 pages, 8769 KiB

Open AccessTechnical Note

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

by Won-Kyung Baek and Hyung-Sup Jung

Remote Sens. 2021, 13(16), 3203; https://doi.org/10.3390/rs13163203 - 12 Aug 2021

Cited by 34 | Viewed by 3462

Abstract

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. Full article

(This article belongs to the Special Issue Selected Papers from the “International Symposium on Remote Sensing 2021”)

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