• Korean Journal of Remote Sensing
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• v.14 no.1
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• pp.69-82
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• 1998

SAR imagery can overcome the limitations of electro-optical sensor imagery and provide us Information which plays a supplementary role. But it is necessary to remove a variety of geometric errors in SAR imagery. An accurate geometric correction of SAR imagery is not easy task to achieve, though some techniques and theories are introduced. We also have difficulties such as transformation problem between 'International' ellipsoid in Radarsat system and 'Bessel' ellipsoid. Two widely used correction method, one is made by simulated image, and the other by collinearity equation, usually use DEM. In this study, the merits and demerits of geocoding methods respectively and the effective method for Korean terrain were found.

• Korean Journal of Remote Sensing
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• v.38 no.6_4
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• pp.1955-1969
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• 2022

Most of the public and private users who use national satellite information such as the KOMPSAT series mainly use Electro-Optical and Infrared (EO/IR) satellite images, and the utilization of Synthetic Aperture Radar (SAR) images is relatively insufficient. As KOMPSAT-5 currently in operation, KOMPSAT-6 and micro SAR satellite constellation systems are scheduled to be launched in the future, the demand for utilization of SAR satellite information is increasing in various fields. Accordingly, it is necessary to possess core technology for SAR utilization that can support the utilization of SAR satellite information for users. Due to the all-weather properties of SAR system, change detection technology is a key application technology. However, until now, the development of technology that automatic change detection and cueing using SAR images is insufficient. Through this study, the requirements of automatic change detection and cueing function using multi-temporal KOMPSAT-5 SAR satellite images were derived and a prototype program was developed. This prototype program aims to secure independent SAR utilization technology and promote the utilization of domestic SAR satellite information by practitioners in public sector organizations in Korea.

• Korean Journal of Remote Sensing
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• v.33 no.6_3
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• pp.1215-1221
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• 2017

SeNtinel's Application Platform (SNAP) is an open source software developed by the European Space Agency and consists of several toolboxes that process data from Sentinel satellite series, including SAR (Synthetic Aperture Radar) and optical satellites. Among them, S1TBX (Sentinel-1 ToolBoX)is mainly used to process Sentinel-1A/BSAR images and interferometric techniques. It provides flowchart processing method such as Graph Builder, and has convenient functions including automatic downloading of DEM (Digital Elevation Model) and image mosaicking. Therefore, if computer memory is sufficient, InSAR (Interferometric SAR) and DInSAR (Differential InSAR) perform smoothly and are widely used recently in the world through rapid upgrades. S1TBX also includes existing SAR data processing functions, and since version 5, the processing capability of KOMPSAT-5 has been added. This paper shows an example of processing the interference technique of KOMPSAT-5 SAR image using S1TBX of SNAP. In the open mine of Tavan Tolgoi in Mongolia, the difference between DEM obtained in KOMPSAT-5 in 2015 and SRTM 1sec DEM obtained in 2000 was analyzed. It was found that the maximum depth of 130 meters was excavated and the height of the accumulated ore is over 70 meters during 15 years. Tidal and topographic InSAR signals were observed in the glacier area near Jangbogo Antarctic Research Station, but SNAP was not able to treat it due to orbit error and DEM error. In addition, several DInSAR images were made in the Iraqi desert region, but many lines appearing in systematic errors were found on coherence images. Stacking for StaMPS application was not possible due to orbit error or program bug. It is expected that SNAP can resolve the problem owing to a surge in users and a very fast upgrade of the software.

• Journal of Satellite, Information and Communications
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• v.12 no.1
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• pp.6-12
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• 2017

Recently, the demand for SAR imaging is growing to monitor natural disasters or military sites to foresee topographic changes, where optical sensing is not easily available. High-resolution SAR images are useful in exploring topography and monitoring artificial land objects in all weather conditions. In this paper,high resolution SAR images acquired from KOMPSAT-5 are exploited for the applications of change detection and classification. In order to detect change areas, amplitude change detection (ACD) and coherence change detection (CCD) algorithms are employed and their performances are compared in practical applications. For enhanced performance, the potential of small scaled change detection is explored by combining multi-temporary SAR images. The k-means and SVM methods are applied for land classifications and their performances are compared by applying to the real spaceborne SAR images.

• Journal of Korea Multimedia Society
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• v.25 no.1
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• pp.1-9
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• 2022

Ship detection in synthetic aperture radar (SAR) images is an important application in marine monitoring for the military and civilian domains. Over the past decade, object detection has achieved significant progress with the development of convolutional neural networks (CNNs) and lot of labeled databases. However, due to difficulty in collecting and labeling SAR images, it is still a challenging task to solve SAR ship detection CNNs. To overcome the problem, some methods have employed conventional data augmentation techniques such as flipping, cropping, and affine transformation, but it is insufficient to achieve robust performance to handle a wide variety of types of ships. In this paper, we present a novel and effective approach for deep SAR ship detection, that exploits label-rich Electro-Optical (EO) images. The proposed method consists of two components: a data augmentation network and a ship detection network. First, we train the data augmentation network based on conditional generative adversarial network (cGAN), which aims to generate additional SAR images from EO images. Since it is trained using unpaired EO and SAR images, we impose the cycle-consistency loss to preserve the structural information while translating the characteristics of the images. After training the data augmentation network, we leverage the augmented dataset constituted with real and translated SAR images to train the ship detection network. The experimental results include qualitative evaluation of the translated SAR images and the comparison of detection performance of the networks, trained with non-augmented and augmented dataset, which demonstrates the effectiveness of the proposed framework.

• Korean Journal of Remote Sensing
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• v.38 no.2
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• pp.153-166
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• 2022

Waterstorage is one of the factorsthat most directly represent the amount of available water resources. Since the effects of drought can be more intuitively expressed, it is also used in variousstudies for drought evaluation. In a recent study, hydrological drought was evaluated through information on observing reservoirs with optical images. The short observation cycle and diversity of optical satellites provide a lot of data. However, there are some limitations because it is vulnerable to the influence of weather or the atmospheric environment. Therefore, thisstudy attempted to conduct a study on estimating the drought index using Synthetic Aperture Radar (SAR) image with relatively little influence from the observation environment. We produced the waterbody of Baekgok and Chopyeong reservoirs using SAR images of Sentinel-1 satellites and calculated the Reservoir Area Drought Index (RADI), a hydrological drought index. In order to validate the applicability of RADI to drought monitoring, it was compared with Reservoir Storage Drought Index (RSDI) based on measured storage. The two indices showed a very high correlation with the correlation coefficient, r=0.87, Area Under curve, AUC=0.97. These results show the possibility of regional-scale hydrological drought monitoring of SAR-based RADI. As the number of available SAR images increases in the future, it is expected that the utilization of drought monitoring will also increase.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1791-1799
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• 2023

The Korea Aerospace Research Institute is responsible for supplying and supporting the utilization of imagery data from the Arirang satellite series for organizations affiliated with the Government Satellite Information Application Consultation. Most of them primarily utilize optical imagery, and there is a relative lack of utilization of Synthetic Aperture Radar (SAR) imagery. In this paper, as part of supporting the use of SAR images, we investigated SAR intensity-based change detection algorithms and their use cases that have been researched to determine SAR intensity-based change detection algorithms to be developed in the future. As a result of the research, we found that various algorithms utilizing intensity difference, correlation coefficients, histograms, or polarimetric information have been researched by numerous researchers to detect and analyze change pixels and the applications of change detection algorithms have been studied in various fields such as a city, flood, forest fire, and vegetation. This study will serve as a reference for the development of SAR change detection algorithms, intended for utilization in the Government Satellite Information Application Consultation.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.107-109
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• 2003

Optical sensor-based land cover categories produce spectral signature confusion along with degraded classification accuracy. In the classification tasks, the goal of fusing data from different sensors is to reduce the classification error rate obtained by single source classification. This paper describes the result of land cover/land use classification derived from solely of Landsat TM (TM) and multisensor image fusion between JERS 1 SAR (JERS) and TM data. The best radar data manipulation is fused with TM through various techniques. Classification results are relatively good. The highest Kappa Coefficient is derived from classification using principal component analysis-high pass filtering (PCA+HPF) technique with the Overall Accuracy significantly high.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.128-133
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• 1998

To assess the flood damages and to provide necessary information for preventing future catastrophe, it is necessary to appraise the inundated area with more accurate and rapid manner. This study attempts to evaluate the potential of satellite synthetic aperture radar (SAR) data for mapping of flood inundated area in southern part of Korea. JERS L-band SAR data obtained during the summer of 1997 were used to delineate the inundated areas. In addition, Landsat TM data were also used for analyzing the land cover condition before the flooding. Once the two data sets were co-registered, each data was separately classified. The water surface areas extracted from the SAR data and the land cover map generated using the TM data were overlaid to determine the flood inundated areas. Although manual interpretation of water surfaces from the SAR image seems rather simple, the computer classification of water body requires clear understanding of radar backscattering behavior on the earth's surfaces. It was found that some surface features, such as rice fields, runaway, and tidal flat, have very similar radar backscatter to water surface. Even though satellite SAR data have a great advantage over optical remote sensor data for obtaining imagery on time and would provide valuable information to analyze flood, it should be cautious to separate the exact areas of flood inundation from the similar features.

• Korean Journal of Remote Sensing
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• v.38 no.4
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• pp.365-374
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• 2022

Mining activity causes environmental pollution and geological hazards such as ground subsidence or landslide of which continuous monitoring is necessary. In this study, the activity on the Fushun West Open-Pit Mine (FWOPM), one of the largest open-pit coal mines in Asia located in Fushun, Liaoning Province, China, was analyzed by using a time-series Sentinel-1 InSAR coherence dataset. By using the difference between the two Digital Elevation Models (DEM) of the area, it was possible to confirm that there was a stockpiling activity in the western area of the FWOPM while excavation activity in the eastern area. By using RGB composite images using the yearly-averaged InSAR coherence images, the activity of the mine was confirmed by period, which was confirmed by Google Earth optical images. As a result, it was possible to confirm three landslides and the related activities on the northwest slope and the dumping activity on the west slope of FWOPM.