Korean Journal of Remote Sensing

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Comparison of Deep Learning-Based SAR-to-Optical Image Translation Models for High Spatial Resolution Optical Image Restoration

Despite the increased availability of high spatial resolution satellite images with high temporal resolution, including micro-satellite constellations, the restoration of missing regions due to clouds and cloud shadows in optical imagery remains crucial for constructing optical image time series. The translation of synthetic aperture radar (SAR) imagery into optical imagery, known as SAR-to-optical image translation, has been effectively applied for optical image restoration. However, few studies have applied SAR-to-optical image translation to restore missing regions in high spatial resolutio...

Korean J. Remote Sens. 2024; 40(6): 881-893

Comparative Analysis of Image Segmentation Performance Based on Batch Size Variations in UNet Based Deep Learning Models

Semantic image segmentation techniques have recently gained widespread adoption in the field of remote sensing for tasks such as classifying surface properties and extracting specific objects. The performance of semantic image segmentation is influenced not only by the choice of deep learning model but also by the configuration of key hyperparameters, including learning rate and batch size. Among these hyperparameters, the batch size is typically set to a larger value to improve model performance. However, since the processing capacity of a typical deep learning system’s graphics process...

Korean J. Remote Sens. 2024; 40(6): 895-906

Generation of Simulated Satellite Images for the CAS500-4 by Inverse Orthorectification

The Compact Advanced Satellite 500-4 (CAS500-4) is scheduled for launch in 2025 and is expected to play a significant role in monitoring agricultural and forest resources across the Korean Peninsula. However, the absence of actual CAS500-4 data prior to launch presents challenges for pre-launch research and verification, which constrains the ability to reflect the distinctive attributes of the satellite accurately. To address this issue, this study proposes a method for generating CAS500-4 Level-1 Radiometric (L1R) simulated images by inverse orthorectification based on the rational function m...

Korean J. Remote Sens. 2024; 40(6): 907-917

Analysis of Magma Activity during the Cerro Azul Volcanic Unrest in March 2017 Using Sentinel-1 DInSAR Observations

Cerro Azul volcano, one of the most active volcanoes in the Galápagos Archipelago, exhibited significant unrest in March 2017, marked by dramatic displacement around the caldera and on the southeastern plain. This study applied differential interferometric synthetic aperture radar (DInSAR) technique to SAR image pairs acquired from the Sentinel-1A satellite’s ascending and descending nodes to measure the line-of-sight (LOS) and two-dimensional (up-down and east-west) displacements during the 2017 unrest of the volcano. Extensive concentric fringe patterns were observed around the ...

Korean J. Remote Sens. 2024; 40(6): 919-930

Evaluating Rooftop and Offshore Photovoltaic Power Potential for Achieving Energy Self-Sufficiency in Hataedo Island, South Korea

Islands rely heavily on diesel generation for power supply due to the absence of a power grid connection to the mainland, making it a significant source of greenhouse gas emissions in these areas. An energy-self-sufficient island is a model that aims to achieve decarbonization by replacing diesel power, the primary energy source in island regions, with renewable energy. This study evaluates potential sites for solar power generation on building rooftops and offshore areas. It estimates the solar power generation potential on Hatado Island, Heuksan-myeon, Shinan-gun, Jeollanam-do, South Korea, ...

Korean J. Remote Sens. 2024; 40(6): 931-942

Small Ship Detection for Marine Search and Rescue: Construction of High-Resolution Training Data and Performance Evaluation Using Microsatellite Imagery

Maritime accidents cause human and property losses, making timely detection of small ships crucial for improving rescue operations’ efficiency. To address this, this study constructed a high-resolution training dataset for small ship detection using satellite imagery and evaluated the performance of various deep learning models. Among the detection transformer (DETR) models, the DETR with improved denoising anchor boxes for end-to-end object detection (DINO) model achieved an average precision (AP) of 0.934, outperforming convolutional neural network (CNN)-based models. Notably, it succe...

Korean J. Remote Sens. 2024; 40(6): 943-955

Development of Spatio-Temporal Gap-Filling Technique for NDVI Images

Time-series Normalized Difference Vegetation Index (NDVI) data extracted from optical satellite images is the most widely used data for predicting crop growth and yield. In the time series pattern of NDVI, variation due to the growth cycle of crops and reduction due to clouds are often seen. In particular, the rainy season in Korea is an important growing period for crops, but it is very difficult to use optical satellite images due to many clouds. In this study, a spatio-temporal gap-filling technique was developed for Sentinel-2A/B NDVI images obtained in rice paddy in Dangjin. The spatial g...

Korean J. Remote Sens. 2024; 40(6): 957-963

Slip Decision Algorithm and Power Consumption Analysis of Lunar Exploration CubeRover Using Star Motion Blurring on Cortex-Based Embedded PC

This study proposes a camera-vision-based method for slip perception and decision through image preprocessing utilizing star motion blurring phenomena to enable safe navigation for exploration rovers on hazardous terrains, such as soft sand, which may induce slippage. Aiming for real-time performance in low-power embedded environments, we developed a Cortex-based printed circuit board. Through evaluations on validation datasets, the slip detection algorithm showed that the contour tracing technique achieved 31% higher accuracy compared to the Hough transformation. In addition, frames per secon...

Korean J. Remote Sens. 2024; 40(6): 965-973

Comparative Analysis of Row Gradient and BRDF Corrections in UAV’s Multispectral Camera under Varied Cloud Cover

Remote sensing technology has significantly enhanced crop growth and disease monitoring. While satellites operate above the clouds, Unmanned Aerial Vehicle (UAV) mostly operate below the clouds, making cloud cover a critical factor. This study aims to identify the best surface reflectance correction method for UAV images under Sky Clear (SKC) and Broken Clouds Sky (BKN) conditions by comparing NoProcess, New Row Gradient (NewROW), and Bidirectional Reflectance Distribution Function (BRDF) methods. Data was collected using a RedEdge MX camera, and the accuracy of the data results was validated ...

Korean J. Remote Sens. 2024; 40(6): 975-989

Enhancing Wildfire Damage Detection Performance with Swin Transformer Using PlanetScope and Sentinel-2 Satellite Imagery: Analysis of Multi-Source Data Fusion

Wildfire damage detection has a pivotal role in forest management and recovery strategy planning. This study demonstrates that the use of a deep learning Swin (shifted windows) Transformer model, by integrating high-resolution PlanetScope and Sentinel-2 satellite imagery, can effectively enhance wildfire damage detection performance. A 5-fold cross-validation was conducted on five major wildfire areas in South Korea, with independent detection performance evaluated for each region to assess the model’s reliability and generalization. The comparison between traditional geometric augmentat...

Korean J. Remote Sens. 2024; 40(6): 991-1004

GCP Patch-Based Automatic Georeferencing of Drone Images

Ground control points (GCPs) are essential for the precise georeferencing of drone imagery, but traditional manual measurement methods are inefficient and resource-intensive. Research on automated georeferencing methods utilizing GCP patches is necessary to address this issue. The purpose of this study is to propose and validate an automated method that improves georeferencing accuracy for drone imagery by leveraging GCP patches. The proposed method combines iterative automatic measurement and bundle adjustment to improve measurement accuracy incrementally. A Random Forest-based binary classif...

Korean J. Remote Sens. 2024; 40(6): 1005-1017

Validation of Spatial Boundary of the Ulleung Warm Eddy Using Altimetry

Ocean eddies play an important role in transferring and releasing thermal energy and momentum through horizontal and vertical advections. In order to understand their motions, the spatial boundaries are important to determine. Thus, this study validated the spatial boundaries of intrathermocline Ulleung Warm Eddy (UWE) as a case study. The surface spatial boundary was determined based on the Lagrangian Particle Tracking (LPT) method, which was evaluated based on subsurface thermal profile measurements collected by the National Institute of Fisheries Science (NIFS). We found that first, the sur...

Korean J. Remote Sens. 2024; 40(6): 1019-1026

Addressing Concerns to Preparing Policies on Satellite Data Openness, Licensing, and Pricing in Korea

Barings designs, secures, operates, and licenses satellites to monitor its members' environments and produces large amounts and types of satellite data, all publicly available. Ownership, usage rights, and interest in derivative works of information acquired with significant national legal input also present different competition across countries. The purpose of this study is to analyze the current status of satellite image use, licensing policy, and pricing policy and to clarify topics that require discussion. We investigated overseas cases, including advanced countries that distribute satell...

Korean J. Remote Sens. 2024; 40(6): 1027-1038

Outlier Detection in Crowdsourced LiDAR Data Using Deep Learning-Based Semantic Segmentation

Crowdsourced drone LiDAR data, collected from diverse sensors and environments, often suffer from inconsistent data quality. Outliers in such datasets can distort the characteristics of terrain and structures, leading to inaccurate decision-making. This study aims to validate the quality of crowdsourced LiDAR data by developing a precise outlier detection method based on semantic segmentation. Using the open-source Semantic Terrain Points Labeling Synthetic 3D (STPLS3D) dataset, noise-augmented training data were generated through simulations. Subsequently, a Kernel Point Convolution (KPConv) ...

Korean J. Remote Sens. 2024; 40(6): 1039-1049

Transformer-Based Deep Learning Approach for Wildfire Burn Severity Estimation Using Drone Imagery in South Korea

The frequency and scale of large wildfires are increasing worldwide due to the rise in extreme weather events. Wildfires not only cause direct damage to human life and property but also lead to complex environmental issues, including forest ecosystem destruction and acceleration of climate change through massive greenhouse gas emissions. In South Korea, where forests cover 70% of the territory, accurate assessment and effective management of wildfire damage have emerged as critical challenges. Notably, 99.5% of domestic wildfires are small to medium fires under 100 ha, and their cumulative imp...

Korean J. Remote Sens. 2024; 40(6): 1051-1065

Landslide Detection Using Swin Transformer Model and PlanetScope Images: A Performance Comparison by the Input Data Combination

Landslide areas have the limitation of less distinct spectral characteristics compared to other land cover types. To address this, combining spectral information provided by satellite imagery with other types of information can improve the performance of deep learning-based landslide detection. This study analyzes the performance differences of a deep learning model when various combinations of spectral information, vegetation information, topographic information, and texture information are used. The model used in this study is the Swin Transformer, which is known for its high performance in ...

Korean J. Remote Sens. 2024; 40(6): 1067-1077

Optimal Route Generation and Performance Evaluation for Manned Aircraft-Based Ship Detection in Maritime Search and Rescue

Maritime Search and Rescue (SAR) operations are inherently challenging due to the vast and unpredictable nature of the ocean environment, which presents complexities such as large search areas, dynamic environmental conditions, and limited visibility. The critical nature of these operations necessitates rapid and efficient response capabilities, yet traditional search methods often fall short in addressing these challenges effectively. To address these limitations, this study introduces a novel framework for optimizing flight paths of manned aircraft, aiming to significantly enhance detection ...

Korean J. Remote Sens. 2024; 40(6): 1079-1093

Machine Learning-Based Mapping of Daily Methane Concentration in Rice Paddies Using Meteorological Data and Satellite Images: A Case of South Korea

Methane from rice fields has a strong greenhouse effect and its accurate estimation is essential to combat climate change. In this study, we conducted an analysis based on the Gradient Boosting Machine (GBM) model using Local Data Assimilation and Prediction System (LDAPS) data, Normalized Difference Vegetation Index (NDVI), and Normalized Difference Water Index (NDWI) from VIIRS and Moderate Resolution Imaging Spectroradiometer (MODIS), and FluxNet ground observations of the Cheorwon rice paddy region. This was used to estimate methane emissions from rice paddy fields in South Korea and to cr...

Korean J. Remote Sens. 2024; 40(6): 1095-1108

Impact of COVID-19 on the Urban Heat Island in Daegu Using Downscaled Land Surface Temperature

The COVID-19 pandemic significantly reduced human activities globally, leading to various changes in urban environments. Previous studies analyzing the pandemic’s impact on urban heat islands (UHI) have heavily relied on the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) dataset with a spatial resolution of 1 km. However, such a coarse resolution of MODIS failed to adequately capture the complexity of urban structures, making it difficult to explore the change of UHI accurately. The objective of this study is to analyze the impact of changes in human...

Korean J. Remote Sens. 2024; 40(6): 1109-1125

Sub-District-Level Heat Vulnerability Assessment Using ECOSTRESS: A Case Study of Busan and Daegu Metropolitan Cities

The intensification of heatwaves and urban heat island effects due to recent climate change has raised concerns about public health and economic losses. This study quantitatively evaluated heat vulnerability using the Heat Vulnerability Index (HVI) for Busan and Daegu, two metropolitan cities in South Korea. ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) data were utilized to derive heat exposure variables that reflect the spatiotemporal variability of the thermal environment. These were combined with sensitivity and adaptive capacity variables, extracted throu...

Korean J. Remote Sens. 2024; 40(6): 1127-1139

Ship Detection in Dual-Polarization SAR Imagery Based on YOLO and Late Fusion

With the recent development of satellites and microsatellite constellations, higher temporal resolution satellite imagery and larger numbers of satellites have become available. As the availability of satellite data increases, it is necessary to develop ship detection models that consider specific data characteristics, such as polarization, spatial resolution, and frequency bands. Among these, to leverage the distinct features of polarization, we employed a late fusion approach to merge the ship detection results from Sentinel-1 dual-polarization data. To evaluate the effectiveness of the fusi...

Korean J. Remote Sens. 2024; 40(6): 1141-1148

Prediction of Water Storage Rate for Agricultural Reservoirs Using Recurrent Neural Network and Transformer Time Series Models

In Korea, there are 17,100 agricultural reservoirs that play the role of agricultural water supply and flood prevention, but more than 13,700 of them are not equipped with hydrological measurement facilities, making it difficult to operate properly. On the other hand, research on detecting reservoir water volume using satellite imagery and converting it into water yield is actively underway, and if the water yield prediction model can predict the water yield of agricultural reservoirs with high accuracy, it can enable effective operation of measured and unmeasured reservoirs. In this study, we...

Korean J. Remote Sens. 2024; 40(6): 1149-1162

Dataset Construction and Semantic Segmentation of Buildings and Roads from CAS500-1 Images

The current status of buildings and roads is an essential element of land monitoring and management, and is used in various fields such as urban planning, disaster management, and change detection. With the recent advancement of deep learning technology, high-performance semantic segmentation techniques have become commonplace, and research on applying them to building and road detection has begun to be actively conducted. However, most of these studies are conducted overseas, so there are still limitations in applying them to Korean field. Therefore, in this study, we constructed a building a...

Korean J. Remote Sens. 2024; 40(6): 1163-1176

Development of Administrative District-Based Water Body Monitoring System Using Sentinel-1 SAR and Swin Transformer in South Korea

This study develops an integrated water body monitoring system at the administrative district level using synthetic aperture radar (SAR) images. A semantic segmentation model based on the Swin transformer was constructed and trained on a dataset of 1,043 patches collected from 2018 to 2023. The 5-fold cross-validation results showed an average Intersection over Union (IoU) of 0.9311, precision of 0.9637, and recall of 0.9576, with low variability between folds (IoU standard deviation of 0.0181). The practical applicability of the administrative district-based water body detection system was ev...

Korean J. Remote Sens. 2024; 40(6): 1177-1193

AI-Based Paddy Rice Yield Prediction Using Satellite Images, Meteorological Data, and Digital Elevation Model: Case Study of South Korea, 2000–2023

The increasing uncertainty in crop production caused by climate change underscores the necessity of accurate yield predictions for staple crops like rice. This study developed a rice yield prediction model that accounts for climate change by integrating satellite imagery and artificial intelligence. Key variables, including temperature, precipitation, and solar radiation, were derived from Korea Meteorological Administration Automated Synoptic Observing System (ASOS) data and combined with NASA’s Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM) to incorporate topograph...

Korean J. Remote Sens. 2024; 40(6): 1195-1208

Analysis of Urban Vitality in Democratic People’s Republic of Korea Using Geospatial Data and Nighttime Light Satellite Imagery

Urban vitality is an important indicator for assessing the growth potential of cities and the quality of life of citizens. Its significance is increasingly recognized due to its ability to reflect the diverse characteristics of urban environments. In the case of Democratic People’s Republic of Korea (DPRK), the unique political system and social ideology are deeply embedded in urban spaces and society, making it difficult to fully understand the conditions through socioeconomic indicators alone. Therefore, analyzing urban vitality is essential for gaining a deeper understanding of DPRK. ...

Korean J. Remote Sens. 2024; 40(6): 1209-1217

Enhanced Vehicle Detection and Segmentation Using the SAMRS Model: Applications in High-Resolution Satellite Imagery

Deep learning technologies have revolutionized image processing and analysis, introducing groundbreaking innovations that significantly improve the accuracy and efficiency of object segmentation, especially in satellite imagery. The increasing availability of high-resolution satellite images has created a demand for advanced models capable of handling the complexities of object detection in diverse environments. This study investigates the potential of the Segment Anything Model for Remote Sensing (SAMRS), a deep learning framework specifically designed for remote sensing applications, to accu...

Korean J. Remote Sens. 2024; 40(6): 1219-1227

Correlation-Based Evaluation of GEMS Satellite Products Using Environmental and Meteorological Measurements

This study evaluated the reliability and applicability of the Geostationary Environment Monitoring Spectrometer (GEMS) data for monitoring atmospheric conditions in South Korea. This study analyzed correlations between GEMS satellite products (Aerosol Optical Depth [AOD], NO2, HCHO, Ozone Profile [O3P]), Airkorea ground-based data, Local Data Assimilation and Prediction System (LDAPS) meteorological data, and land cover map across 161 administrative districts from 2021 to 2023. Despite the satellite’s low spatial resolution, analysis revealed significant correlations between GEMS observa...

Korean J. Remote Sens. 2024; 40(6): 1229-1252

A Study on the Planning Methodology of Geographic Education Contents Using Satellite Information Data: Case Studies of Geography Education Content in Incheon and Jeju Island

This study explores ways to expand the area of use of satellite information data and increase its value. Specifically, on the premise of using satellite information data in the field of geography education, a methodology for planning geography education content is presented. For the research methodology, domestic and foreign literature surveys and cases in the content area were investigated and analyzed. According to domestic and international case studies, education was provided to analyze changes in the urban environment by applying Global Positioning System (GPS) and Geographic Information ...

Korean J. Remote Sens. 2024; 40(6): 1253-1264

Development and Application of Multispectral Drone Image Pre-Processing Module for Agricultural Monitoring

In order to address issues related to agricultural productivity and sustainability caused by climate change, the importance of agricultural observation monitoring is increasing. Remote sensing data at the field, county, and national levels are being collected using various remote sensing platforms. In particular, drone imagery is becoming an attractive platform with high resolution compared to existing satellite and aerial imagery, allowing for observation of desired regions at specific times, thus enabling immediate responses to agricultural environmental changes. Pre-processing for crop cond...

Korean J. Remote Sens. 2024; 40(6): 1265-1273

Evaluation of the DROACOR Model for Atmospheric Correction of Drone Hyperspectral Data

As the use of drone-based hyperspectral data increases, atmospheric correction methods that are suitable for the characteristics of drone data are required. In this letter, we evaluated the performance of the drone atmospheric correction (DROACOR) model for atmospheric correction of drone hyperspectral data by comparing it with the results of empirical line correction. The results using the normalized difference vegetation index and Fe3+ index showed a high correlation (R2=0.99) between the two correction models in the visible/near-infrared (VNIR). However, the DROACOR model estimated the over...

Korean J. Remote Sens. 2024; 40(6): 1275-1281

A Spatio-Temporal Analysis of Surface Albedo, Aerosol Optical Depth and Cloud Optical Thickness over East Asia Using COMS/MI Observations

Land surface albedo, a key component of Earth’s energy balance, is influenced by aerosols and clouds, which impact atmospheric and surface temperatures, ultimately affecting the climate system. This study utilized data from the Communication, Ocean, and Meteorological Satellite (COMS) to analyze the long-term trends of surface albedo, Aerosol Optical Depth (AOD), and Cloud Optical Thickness (COT) over East Asia. From January 2012 to December 2019, surface albedo, AOD, and COT all showed decreasing trends in the region, with rates of -0.0003, -0.0008, and -0.0328, respectively. Spatial di...

Korean J. Remote Sens. 2024; 40(6): 1283-1288

Comparative Analysis of PyTAF and kd-tree Resampling Methods for Geostationary Satellite Data: Performance and Efficiency Insights

This study compares two resampling methods, the k-dimensional tree (kd-tree) and the Python interface for Terra Advanced Fusion Resample/Reprojection functions (PyTAF), using surface albedo data from the GEO-KOMSAT-2A (GK2A) Advanced Meteorological Imager (AMI) geostationary satellite, with and without consideration of Earth’s curvature. Evaluation metrics include Correlation Coefficient (R), Root Mean Square Deviation (RMSD), Relative RMSD (RRMSD), Bias, spatial distribution, and processing time. Additional quantitative analyses were performed based on viewing zenith angle (VZA) interva...

Korean J. Remote Sens. 2024; 40(6): 1289-1294

Remote Sensing and Geo-Spatial Information Convergence Analysis for National Disaster Management

Due to global weather anomalies and climate change, natural disasters are increasingly escalating into extreme catastrophes year by year. The intense summer rains, late autumn heatwaves, and early snowfall in central Korea in November 2024 reflect the extreme climate phenomena and the evolving paradigm of disasters. By utilizing advanced Earth observation platforms and remote sensing technologies, it is possible to effectively predict and prepare for disasters, collect timely observational data to respond in real-time during disaster management and conduct integrated analyses of large-scale di...

Korean J. Remote Sens. 2024; 40(6): 1295-1304

Monitoring and Hazard Area Analysis of Forest Soil Sediment Disaster Using Remote Sensing Data

This study aimed to monitor the forest soil sediment disaster caused by Typhoon Hinnamnor in 2022 in Tohamsan, Gyeongju-si, and Gyeongsangbuk-do and analyze high-risk areas to develop effective disaster prevention strategies. The study used remote sensing data (RSD) from various sources, including PlanetScope and Sentinel satellite images, orthophotos from the National Geographic Information Institute (NGII), and drone images. We identified pre- and post-disaster changes and damage patterns through time-series image analysis. The analysis showed that landslide hazards predominantly flow downst...

Korean J. Remote Sens. 2024; 40(6): 1305-1314

Development of an Integrated Information System to Support Heat Waves Response Policies

Heat wave-related information is important in establishing effective heat wave response policies. The National Disaster Management Research Institute (NDMI) produced a heat distribution map that can observe heat phenomena by estimating the highest daily temperature based on satellite images in 2017 and supports local governments’ heat wave response policies. For effective heat wave management, high-resolution information on areas such as heat distribution maps is also important; however, various information is required to develop national heat wave measures. Currently, information relate...

Korean J. Remote Sens. 2024; 40(6): 1315-1322

A Study on the Error Rate Analysis of Building Data Using Grid Systems Based on Vector and Raster Models

This study utilized the national grid reference system to address the issues associated with irregular administrative boundaries in disaster risk analysis. Additionally, to compare differences based on data analysis methods, building data were employed to examine the distinctions between grid-based vector and raster analysis methods. The vector method offers high resolution and accuracy but has slower analysis speeds and poses challenges in data updates. In contrast, the raster method provides faster analysis speeds but lower resolution. When analyzing error rates using grid-based building dat...

Korean J. Remote Sens. 2024; 40(6): 1323-1336

LPI Prediction and Map Expression Using Deep Learning Techniques

In South Korea, earthquakes of magnitude 4.0 and above continue to occur, yet there remains a lack of awareness regarding liquefaction caused by these seismic events. In this context, evaluating liquefaction during seismic events, using geotechnical information from the National Ground Information Portal System consumes considerable human and temporal resources to derive the Liquefaction Potential Index (LPI). Additionally, there are issues concerning the accuracy of the input data. This study designed an LPI prediction model to address these challenges by comparing the calculated LPI with a r...

Korean J. Remote Sens. 2024; 40(6): 1337-1345

Analysis of Landslide Damage from Rainfall Using Drone Mapping

This paper proposed an investigation and analysis plan applying drone mapping technology for landslide damage assessment. Traditional landslide investigation methods are limited by accessibility and require significant time and cost, whereas drone-based methods enable rapid data collection and the generation of high-resolution terrain information. Building on this, the research conducted precise analyses of terrain changes, affected areas, and the scale of damage in landslide-prone regions. It visually represented the characteristics of the damaged areas using 3D models and point clouds, confi...

Korean J. Remote Sens. 2024; 40(6): 1347-1357

Performance Evaluation of Drone LiDAR Sensors for Field Operations in Mountainous Areas

This study evaluated the performance of LiDAR sensors by mounting the DJI Zenmuse L1 and L2 on a DJI Matrice 350 RTK drone to capture ground point cloud data over flat terrain and forested areas. Scans were conducted at altitudes of 50 m and 80 m, with 70 cm × 70 cm aerial targets placed on open ground and under forest vegetation to measure point density. We assessed the multiple reflections in the captured LiDAR data and reviewed absolute accuracy using ground control points. Results showed that the L2 sensor achieved higher point density than the L1 sensor in open areas and performed w...

Korean J. Remote Sens. 2024; 40(6): 1359-1368

Accuracy Assessment of Terrestrial and Drone LiDAR-Based Model for Disaster Site Investigation

This study aims to evaluate the applicability of ground and drone light detection and ranging (LiDAR) for use in disaster sites where damage to facilities and structures has occurred. For this purpose, 3D data was acquired from a disaster environment building of the Korea Institute of Robot and Technology Convergence, using Terrestrial LiDAR for indoor areas and drone LiDAR for outdoor areas. The indoor data were aligned using different methods (target registration, auto, and visual alignment), while the outdoor data were modeled at two different altitudes (30 m and 50 m), resulting in two sep...

Korean J. Remote Sens. 2024; 40(6): 1369-1378

Analysis of Hotspot Changes in Daegu Metropolitan City in Summer Using Landsat 8 Satellite Images

Increasing temperature due to climate change and the urban heat island effect caused by rapid urbanization can deteriorate the thermal environment in urban areas, leading to negative impacts such as increased heat stress during summer and higher energy consumption. In particular, basin topography is considered an ideal area for studying changes in the thermal environment, as its structure hinders the escape of heat from the city, making these issues more pronounced. In this study, a thermal environment based on the hotspot in the summer of Daegu Metropolitan City, a representative basin topogr...

Korean J. Remote Sens. 2024; 40(6): 1379-1389

KOMPSAT Image Application

The Korea Multi-Purpose Satellite (KOMPSAT) series developed according to the Master Plan for the Promotion of Space Development started with launching the first satellite in December 1999, and five satellites have been launched. Currently, KOMPSAT-3, 3A, and 5 are in operation. The KOMPSAT series equipped with high-resolution optical, synthetic aperture radar (SAR), and middle-wave infrared (MIR) sensors have been utilized in various fields such as land, environment, forestry, agriculture, and disaster over the past 25 years, and have recently been utilized in various studies such as image se...

Korean J. Remote Sens. 2024; 40(6): 1391-1395

A Study on Segmentation of Rural Facilities through Redundant Deep Learning Models Using KOMPSAT Optical Images

Rural facilities such as factories and barns near residential areas cause pollution and civil complaints. They are managed through legal registration, but there are still conflicts and inconsistencies. In Korea, the Rural Spatial Restructuring Act will come into effect in March this year. It will promote long-term rural area construction plans and support new public businesses. In this situation, it is essential to have a means to accurately and efficiently identify the status and changes in rural facilities. In this study, we constructed a dataset containing the Korea Multi-purpose Satellite ...

Korean J. Remote Sens. 2024; 40(6): 1397-1408

Satellite Image-Based Field Compost Detection Using Deep Learning

With the development of agriculture, the illegal management of field compost, a non-point source of pollution, has become a growing concern as a source of water and environmental pollution. However, detection of field compost through field surveys is difficult and costly. Therefore, following the recent increase in research on the detection and management of field compost, this study aims to detect field compost using high-resolution satellite imagery. We collected satellite image data in the blue, green, red, and near-infrared (NIR) bands over agricultural fields in Gyeongsangnam-do. We label...

Korean J. Remote Sens. 2024; 40(6): 1409-1419

Performance Analysis of Semantic Segmentation Models Based on Image Enhancement Techniques for KOMPSAT Satellite Imagery

Advances in the aerospace industry have driven growing research into the use of artificial intelligence for analyzing objects of interest in satellite imagery. Unlike typical 8-bit RGB camera images, however, satellite imagery often contains 16-bit pixel values, which can result in outliers that darken the image. This issue leads to difficulty in object identification and negatively impacts analysis performance. To address this issue, various image enhancement techniques have been proposed, but the effectiveness of each technique depends on the specifics of each satellite and the task to be pe...

Korean J. Remote Sens. 2024; 40(6): 1421-1433

Optimization of RIFT Algorithm for Image Registration of KOMPSAT-3A Mid-Infrared Day and Night Images

Mid-infrared (MIR) image is highly valued across various fields, such as national defense and environmental monitoring, due to its capability to capture temperatures of objects and surfaces. Image registration that unifies coordinates between images is a fundamental process for utilizing multi-temporal satellite images. The radiation-invariant feature transform (RIFT) algorithm is a feature-based matching method that extracts robust matching points to non-linear radiometric distortions in day and night MIR images. However, the original RIFT method has a limitation in detecting a small number o...

Korean J. Remote Sens. 2024; 40(6): 1435-1448

Automatic Training Data Generation Method for Self-Supervised End-to-End Matching Network to Extract Matching Points from Very High-Resolution Satellite Imagery

In this study, we propose an automatic training dataset generation method to build a self-supervised matching network, using an End-to-end approach, to extract matching points between very high-resolution (VHR) satellite images. A homography matrix that transforms the scale, rotation, and translation of a single VHR remote-sensing image is applied to generate reference and sensed image patches. After adjusting the contrast and brightness of the sensed image patch, Gaussian and speckle noise are added, and shading and motion blur effects are applied to give it different characteristics from the...

Korean J. Remote Sens. 2024; 40(6): 1449-1460

An Analysis of Surface Changes due to Volcanic Eruptions Using Coherence Change Detection – A Case Study of the May 2024 Volcanic Eruption in Iceland

Sundhnúkur volcano on the Reykjanes Peninsula, Iceland, erupted on May 29, 2024, spewing a large amount of lava, forming a volcanic plateau and causing surface changes. In this study, the Coherence Change Detection (CCD) technique was applied to Sentinel-1 satellite images to observe surface changes caused by the initial volcanic eruption. The Support Vector Machine (SVM) algorithm was applied to Landsat-9 satellite images to derive the threshold for the CCD technique, and based on this, a damage proxy map and change area data were generated to evaluate the area changed by the volcanic ...

Korean J. Remote Sens. 2024; 40(6): 1461-1474

A Method for Time-Series Change Detection in Urban Areas Based on SHP and Coherence Information from SAR Imagery: Focusing on the Application of KOMPSAT-5 Imagery in Port Areas

In this paper, we proposed a method based on statistically homogeneous pixel (SHP) and coherence information for time series change detection of urban areas using synthetic aperture radar (SAR) images. The proposed framework was applied to KOMPSAT-5 SAR time series data to conduct time series change detection experiments on container yards in port areas. If the algorithm is verified with more SAR images in the future, the algorithm for detecting time series changes in areas of interest using domestic KOMPSAT SAR satellite images will become more generalized and reliable.

Korean J. Remote Sens. 2024; 40(6): 1475-1481

Vessel Velocity-Driven SAR Phase Refocusing for Moving Vessel Recognition

In this study, a synthetic aperture radar (SAR) phase refocusing algorithm using vessel velocity was proposed and applied to three different satellite SAR images in order to enhance the recognition performance of moving vessels. Exploiting the Doppler frequency character of target velocity, it effectively calibrates azimuth defocusing and aids vessel recognition by using the well-focused image as training data. Refocusing performance on vessels exceeded that of the conventional autofocusing algorithm, while vessel recognition accuracy was enhanced after performing the refocusing.

Korean J. Remote Sens. 2024; 40(6): 1483-1491

Geometric Correction of Large-Scale KOMPSAT-3A Images through RPCs Re-Adjustment: Optimization of Bundle Block Adjustment Based on Reference Images

Recently, as the frequency of high-resolution satellite image collection and the amount of data have increased, the demand for image data with temporal and spatial accuracy has been rising in various fields. This image data serves an important role in various applications such as environmental monitoring, urban planning, and disaster management. However, geometric correction using ground control points for individual images is inefficient in terms of time and cost, and its practicality decreases, especially when processing large numbers of images. In this study, we propose a method to efficien...

Korean J. Remote Sens. 2024; 40(6): 1493-1503

SAMBA: Synthetic Data-Augmented Mamba-Based Change Detection Algorithm Using KOMPSAT-3A Imagery

Change detection is essential for applications such as urban planning, environmental monitoring, and disaster response. Despite advancements in high-resolution satellite imagery, accurate change detection remains challenging due to increased landscape heterogeneity and variable atmospheric conditions. The Mamba model, an efficient state-space model-based architecture, has shown promise in capturing spatiotemporal relationships in high-resolution datasets, addressing the limitations of traditional methods that struggle with the diverse appearances of urban structures. This research investigates...

Korean J. Remote Sens. 2024; 40(6): 1505-1521

KSRS

Archives

December 31, 2024

Vol. 40, No. 6, pp. 881-1521

Aims and Scope
Korean Journal of Remote Sensing serves the remote sensing community with the publication of results based on theory, technology and applications of remote sensing fields. Thoroughly interdisciplinary, this journal focuses on the research and development of remote sensing science and technologies and promotes the exchange of expert knowledge among remote sensing researchers as well.

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Current Issue Vol. 40, No. 6, December 2024

Research Article
Comparison of Deep Learning-Based SAR-to-Optical Image Translation Models for High Spatial Resolution Optical Image Restoration

Soyeon Park, Geun-Ho Kwak, Eui Ho Hwang, No-Wook Park

Korean Journal of Remote Sensing 2024; 40(6): 881-893

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Research Article
Comparative Analysis of Image Segmentation Performance Based on Batch Size Variations in UNet Based Deep Learning Models

Tae-Woong Baek, Young-Man Ahn, Chul-Soo Ye

Korean Journal of Remote Sensing 2024; 40(6): 895-906

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Research Article
Generation of Simulated Satellite Images for the CAS500-4 by Inverse Orthorectification

Hongjin Kim, Taejung Kim

Korean Journal of Remote Sensing 2024; 40(6): 907-917

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Research Article
Analysis of Magma Activity during the Cerro Azul Volcanic Unrest in March 2017 Using Sentinel-1 DInSAR Observations

Seohyeon Kim, Hyangsun Han, Jin-Woo Kim, Yeong-Beom Jeon, Seung Chul Lee

Korean Journal of Remote Sensing 2024; 40(6): 919-930

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1057

Effects of Environmental Conditions on Vegetation Indices from Multispectral Images: A Review

Md Asrakul Haque, Md Nasim Reza, Mohammod Ali, Md Rejaul Karim, Shahriar Ahmed, Kyung-Do Lee, Young Ho Khang, Sun-Ok Chung
Review Korean Journal of Remote Sensing 2024; 40(4): 319-341
955

National Disaster Management and Monitoring Using Satellite Remote Sensing and Geo-Information

Jongsoo Park, Hagyu Jeong, Junwoo Lee
Review Korean Journal of Remote Sensing 2024; 40(5): 813-832
563

Application of Geo-Segment Anything Model (SAM) Scheme to Water Body Segmentation: An Experiment Study Using CAS500-1 Images

Hayoung Lee, Kwangseob Kim, Kiwon Lee
Research Article Korean Journal of Remote Sensing 2024; 40(4): 343-350
444

History, Status, and Prospects of Remote Sensing in the Field of Meteorological Satellite in Korea

Sung-Rae Chung, Myoung-Hwan Ahn, Dohyeong Kim, Byung-Il Lee, Daehyeon Oh
Review Korean Journal of Remote Sensing 2024; 40(5): 713-726

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Effects of Environmental Conditions on Vegetation Indices from Multispectral Images: A Review

Md Asrakul Haque, Md Nasim Reza, Mohammod Ali, Md Rejaul Karim, Shahriar Ahmed, Kyung-Do Lee, Young Ho Khang, Sun-Ok Chung
Review Korean Journal of Remote Sensing 2024; 40(4): 319-341
111

Application of Geo-Segment Anything Model (SAM) Scheme to Water Body Segmentation: An Experiment Study Using CAS500-1 Images

Hayoung Lee, Kwangseob Kim, Kiwon Lee
Research Article Korean Journal of Remote Sensing 2024; 40(4): 343-350
104

Assessing the Geometric Integrity of Cylindrical Storage Tanks: A Comparative Study Using Static Terrestrial Laser Scanning and Total Station

Mansour Alghamdi, Jinha Jung
Research Article Korean Journal of Remote Sensing 2024; 40(3): 243-255
101

Automated Updates of Coordinates of Ground Control Points Through Tiepoints from Multiple Satellite Images

Seunghyeok Choi, Seunghwan Ban, Taejung Kim
Research Article Korean Journal of Remote Sensing 2024; 40(5): 419-429

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Announcement

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Call for Papers for the February 2025 Issue (Regular Deadline: Jan 29; Fast Track Deadline: Feb 5)

January 8, 2025
Call for Papers for December 2024 Issue (Submission Closed)

October 9, 2024
Call for Papers - Special Issue on KOMPSAT Image Processing and Applications (Submission Closed)

[Call for Papers]

Special Issue on KOMPSAT Image Processing and Applications

Over the past several deca...

September 20, 2024
Call for Papers for October 2024 Issue (Submission Closed)

Dear Authors,

We are now accepting submissions for the October 2024 issue of the Korean Journal of Remote Sensing (KJRS). Please note that starting ...

September 20, 2024

About the Journal

KSRS Journal

Aims and Scope

Korean Journal of Remote Sensing serves the remote sensing community with the publication of results based on theory, technology and applications of remote sensing fields. Thoroughly interdisciplinary, this journal focuses on the research and development of remote sensing science and technologies and promotes the exchange of expert knowledge among remote sensing researchers as well.

Areas of interest include, but are not necessarily restricted to:
– Remote Sensor Technology
– Novel Sensor Design and Platforms Development ...

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